Re: Wekalist Digest, Vol 172, Issue 44

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Re: Wekalist Digest, Vol 172, Issue 44

micCalve
In my test set can be up to 22 different wifi devices, of which I am capturing network traffic data on, using a WiFi sniffer.  Their class attributes are labeled as question marks in order for weka to classify them.

My question pertains to whether or not the testing accuracy decreases or increases depending on how many of these devices' data (250 instances per device and three attributes per instance) are predicted at one time.  Basically, if I create two test sets, one with 3 devices (750 total instances), and one with 22 devices (5,500 instances), will I get the same results for the three devices that are in both of these test sets???

On Tue, Jun 13, 2017 at 7:45 AM, <[hidden email]> wrote:
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Today's Topics:

   1. Re: Does Amount of Test Classes matter for Binary Random
      Forest Classifier (Eibe Frank)
   2. Look into a model (Thomas Pfau)
   3. Explanation of misclassification (Alexander Osherenko)
   4. how to load a class from an installed package in java     code
      (Ignacio Arganda-Carreras)
   5. Re: Measuring accuracy and efficiency of association      rules!
      (Bhupesh Rawat)


----------------------------------------------------------------------

Message: 1
Date: Tue, 13 Jun 2017 16:37:37 +1200
From: Eibe Frank <[hidden email]>
To: "Weka machine learning workbench list."
        <[hidden email]>
Subject: Re: [Wekalist] Does Amount of Test Classes matter for Binary
        Random  Forest Classifier
Message-ID: <[hidden email]>
Content-Type: text/plain; charset=utf-8

I think you?ll have to explain more precisely what you are doing. Can you give a step by step description?

Cheers,
Eibe

> On 13/06/2017, at 8:25 AM, Michael Calve <[hidden email]> wrote:
>
> Hello,
>
> I am curious if there is a cap for the number of classes can be classified/predicted simulatenously using a Binary Random Forest?
>
> I've currently classifying 22 classes at a time, with 250 instances per class and 3 attributes per instance.  I then use a threshold to decide, based upon the number of instances out of 250, that class has been predicted as.  I use this Binary Random Forest as a filter and then whichever ones pass the threshold, get placed into a testing file for a 6 class Multi Random Forest Classification.
>
> Basically, does the number of classes in the testing set matter?  For both a Binary Random Forest, and a MultiClass Random Forest?
>
> Thanks!
> Michael
> _______________________________________________
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------------------------------

Message: 2
Date: Tue, 13 Jun 2017 07:40:59 +0200
From: Thomas Pfau <[hidden email]>
To: <[hidden email]>
Subject: [Wekalist] Look into a model
Message-ID: <[hidden email]>
Content-Type: text/plain; charset="utf-8"

Hi,

I have a model represented by a voter combination of multiple random
forests. I'm wondering, whether there is any way to actually have a look
at the individual random forests i.e. see what the forests are
doing/which decisions they make.

Best

Thomas

--
Universit? du Luxembourg
Facult? des Sciences, de la Technologie et de la Communication
Campus Belval, Biotech II 115
6 avenue du Swing
L-4367 Belvaux
Tel: <a href="tel:%28%2B352%29%2046%2066%2044%205309" value="+3524666445309">(+352) 46 66 44 5309
Email: [hidden email]



------------------------------

Message: 3
Date: Tue, 13 Jun 2017 00:34:41 -0700 (MST)
From: Alexander Osherenko <[hidden email]>
To: [hidden email]
Subject: [Wekalist] Explanation of misclassification
Message-ID: <[hidden email]>
Content-Type: text/plain; charset=UTF-8

I wonder: are there some articles that aim at the ?error explanation?? of
classification results that consider ?the chosen classifier, the data or
some other aspects and explain the probable reason of misclassification? For
example, a typical answer of this question would be "a classifier works not
good with sparse data" or "a classifier works not good because of data
overfitting".

Best, Alexander



--
View this message in context: http://weka.8497.n7.nabble.com/Explanation-of-misclassification-tp40936.html
Sent from the WEKA mailing list archive at Nabble.com.


------------------------------

Message: 4
Date: Tue, 13 Jun 2017 12:14:38 +0200
From: Ignacio Arganda-Carreras <[hidden email]>
To: "Weka machine learning workbench list."
        <[hidden email]>
Subject: [Wekalist] how to load a class from an installed package in
        java    code
Message-ID:
        <CAE=[hidden email]>
Content-Type: text/plain; charset="utf-8"

Dear all,

I have installed the ClassificationViaClustering classifier using the
package manager on my Weka 3.9.1 and now I would like to instantiate it
from java code. So far I have not been successful and I can only load the
classifier classes that come by default in the weka jar.

This is what I tried:

import weka.core.WekaPackageManager;

WekaPackageManager.loadPackages( true );

Which outputs:

WARNING: core mtj jar files are not available as resources to this
classloader (sun.misc.Launcher$AppClassLoader@764c12b6)
[WekaPackageManager] loading package collective-classification
[WekaPackageManager] loading package classificationViaClustering
Registering weka.classifiers.collective.util.Flipper
weka.gui.GenericObjectEditor
Refreshing GOE props...

So the package seems to be loaded, but then, when I try to instantiate it,
I get a "class not found error":

ClassificationViaClustering classifier = new ClassificationViaClustering();

What am I missing?

Thanks a lot in advance!

ignacio


--
Ignacio Arganda-Carreras, Ph.D.
Ikerbasque Research Fellow
Departamento de Ciencia de la Computacion e Inteligencia Artificial
Facultad de Informatica, Universidad del Pais Vasco
Paseo de Manuel Lardizabal, 1
20018 Donostia-San Sebastian
Guipuzcoa, Spain

Phone : <a href="tel:%2B34%20943%2001%2073%2025" value="+34943017325">+34 943 01 73 25
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Message: 5
Date: Tue, 13 Jun 2017 08:47:54 +0530
From: Bhupesh Rawat <[hidden email]>
To: "Weka machine learning workbench list."
        <[hidden email]>
Subject: Re: [Wekalist] Measuring accuracy and efficiency of
        association     rules!
Message-ID:
        <CAMa3TT1HS=ik+zq5zxAyuxZGWF=[hidden email]>
Content-Type: text/plain; charset="utf-8"

Hi!

I am trying to run predictive apriori algorithm using command line
interface in weka. can you help me with the options of this algorithm.

On Wed, May 31, 2017 at 2:54 PM, Bhupesh Rawat <[hidden email]> wrote:

> Thank your Sir! it worked.
>
> On Wed, May 31, 2017 at 2:47 PM, Eibe Frank <[hidden email]> wrote:
>
>> Yes, the SimpleCLI will also output the elapsed time. Here is how it is
>> measured:
>>
>> startTime = System.currentTimeMillis();
>> associator.buildAssociations(data);
>> endTime = System.currentTimeMillis();
>>
>> Space consumption at runtime cannot easily be measured in WEKA.
>>
>> Cheers,
>> Eibe
>>
>> > On 30 May 2017, at 14:18, Bhupesh Rawat <[hidden email]> wrote:
>> >
>> > I want to compare the performance of apriori,FP growth and Tertius in
>> terms of time and space within weka. moreover what is the use of elapsed
>> time? how it is different from execution time? will smple CLI help to
>> achieve my goal?
>> >
>> > On Mon, May 29, 2017 at 11:25 AM, Eibe Frank <[hidden email]>
>> wrote:
>> > The elapsed time is shown in the output when you run an association
>> rule learner in WEKA from the command-line interface.
>> >
>> > If you want to compare Apriori and PredictiveApriori, which generate
>> different sets of rules, you can use classification based on association
>> rule mining to measure predictive accuracy by installing the
>> classAssociationRules package. This approach was used in
>> >
>> >   Stefan Mutter, Mark Hall, and Eibe Frank. Using classification to
>> evaluate the output of confidence-based association rule mining. In Proc
>> 17th Australian Joint Conference on Artificial Intelligence, Cairns,
>> Australia, pages 538--549. Springer, 2004.
>> >
>> > WEKA?s native implementations of frequent pattern mining algorithms are
>> few in number. You might also want to take a look at the SPMFWrapper
>> package for WEKA.
>> >
>> > Cheers,
>> > Eibe
>> >
>> > > On 29/05/2017, at 2:30 PM, Bhupesh Rawat <[hidden email]> wrote:
>> > >
>> > > could you explain with an example, how to find the running time of an
>> algorithm? moreover I am trying to compare the performance of association
>> rule mining algorithms, could you suggest me some parameters for comparison
>> apart from running time and space.
>> > >
>> > > On Mon, May 29, 2017 at 4:10 AM, Eibe Frank <[hidden email]>
>> wrote:
>> > > It should have some effect on runtime.
>> > >
>> > > I assume you are talking about the number of instances in the data?
>> For Apriori, the number of attributes is often more important, because it
>> determines the number of item set candidates that are available.
>> > >
>> > > Cheers,
>> > > Eibe
>> > >
>> > > > On 29/05/2017, at 1:27 AM, Bhupesh Rawat <[hidden email]> wrote:
>> > > >
>> > > > Hello Weka Users!
>> > > >
>> > > > Although I increased the data size from 50 to 10000,it has no effect
>> > > > on the running time(Apriori algorithm). why?
>> > > >
>> > > > On 4/20/17, Bhupesh Rawat <[hidden email]> wrote:
>> > > >> Thank you Sir!
>> > > >> On Apr 20, 2017 3:52 PM, "Eibe Frank" <[hidden email]> wrote:
>> > > >>
>> > > >>> Because those algorithm implementations are not able to deal with
>> the
>> > > >>> particular data you have loaded. For example, the FPGrowth
>> implementation
>> > > >>> cannot deal with nominal attributes that have more than two
>> values. You
>> > > >>> can
>> > > >>> click the "Capabilities" button in the "GenericObjectEditor" for a
>> > > >>> particular associator to see what kind of data it can deal with.
>> > > >>>
>> > > >>> Cheers,
>> > > >>> Eibe
>> > > >>>
>> > > >>>> On 19 Apr 2017, at 19:02, Bhupesh Rawat <[hidden email]>
>> wrote:
>> > > >>>>
>> > > >>>> I am using weka 3.8.1. Why some association rules algorithms are
>> > > >>> disabled when i apply them to the dataset.
>> > > >>>>
>> > > >>>> On Wed, Apr 19, 2017 at 4:03 AM, Eibe Frank <[hidden email]>
>> wrote:
>> > > >>>> You will be able to see, for example, which words co-occur
>> frequently.
>> > > >>>>
>> > > >>>> Cheers,
>> > > >>>> Eibe
>> > > >>>>
>> > > >>>>> On 19/04/2017, at 5:46 AM, Bhupesh Rawat <[hidden email]>
>> wrote:
>> > > >>>>>
>> > > >>>>> What kind of possible useful results can be found after
>> > > >>>>> experimentally
>> > > >>>>> comparing  various association rules mining algorithms(apriori,
>> > > >>>>> tertius  and predictive) to the e-learning data.
>> > > >>>>>
>> > > >>>>> On 4/11/17, Eibe Frank <[hidden email]> wrote:
>> > > >>>>>> You could create a dataset with a single string attribute
>> holding
>> > > >>>>>> the
>> > > >>> text
>> > > >>>>>> of each web page, apply the StringToWordVector filter,
>> followed by
>> > > >>>>>> NumericToNominal, and then apply Apriori.
>> > > >>>>>>
>> > > >>>>>> Cheers,
>> > > >>>>>> Eibe
>> > > >>>>>>
>> > > >>>>>>> On 10/04/2017, at 11:11 PM, Bhupesh Rawat <[hidden email]>
>> wrote:
>> > > >>>>>>>
>> > > >>>>>>> How to use the string dataset for the task of information
>> > > >>>>>>> extraction
>> > > >>> from
>> > > >>>>>>> web pages with Apriori algorithm in Weka?
>> > > >>>>>>>
>> > > >>>>>>> On Sat, Apr 8, 2017 at 4:41 PM, Bhupesh Rawat <
>> [hidden email]>
>> > > >>> wrote:
>> > > >>>>>>> Thanks Eibe.
>> > > >>>>>>>
>> > > >>>>>>> On Apr 8, 2017 2:57 PM, "Eibe Frank" <[hidden email]>
>> wrote:
>> > > >>>>>>> It's available in a separate package:
>> > > >>>>>>>
>> > > >>>>>>> http://weka.sourceforge.net/packageMetaData/tertius/index.ht
>> ml
>> > > >>>>>>>
>> > > >>>>>>> You can install this package with the WEKA package manager.
>> > > >>>>>>>
>> > > >>>>>>> Cheers,
>> > > >>>>>>> Eibe
>> > > >>>>>>>
>> > > >>>>>>>> On 8 Apr 2017, at 19:41, Bhupesh Rawat <[hidden email]>
>> wrote:
>> > > >>>>>>>>
>> > > >>>>>>>> Where can i find the Tertitus algorithm(association rule
>> mining)
>> > > >>>>>>>> in
>> > > >>> weka
>> > > >>>>>>>> 3.8.1.
>> > > >>>>>>>>
>> > > >>>>>>>> On Sun, Apr 2, 2017 at 9:02 AM, Eibe Frank <
>> [hidden email]>
>> > > >>> wrote:
>> > > >>>>>>>> Possibly, I'm not familiar with those methods. However, if
>> you
>> > > >>>>>>>> have
>> > > >>>>>>>> Boolean data, you might just want to apply a conventional
>> > > >>> association
>> > > >>>>>>>> rule miner anyway.
>> > > >>>>>>>>
>> > > >>>>>>>> Cheers,
>> > > >>>>>>>> Eibe
>> > > >>>>>>>>
>> > > >>>>>>>>> On 2 Apr 2017, at 14:54, Bhupesh Rawat <[hidden email]>
>> wrote:
>> > > >>>>>>>>>
>> > > >>>>>>>>> Is it possible to apply fuzzy logic to boolean data?
>> > > >>>>>>>>>
>> > > >>>>>>>>> On Sun, Apr 2, 2017 at 5:32 AM, Eibe Frank <
>> [hidden email]>
>> > > >>> wrote:
>> > > >>>>>>>>> I would treat this as a classification problem and build a
>> > > >>> per-course
>> > > >>>>>>>>> model that estimates the probability that a student will
>> take a
>> > > >>>>>>>>> particular course given that they have taken a particular
>> set of
>> > > >>>>>>>>> courses already.
>> > > >>>>>>>>>
>> > > >>>>>>>>> I don't think fuzzy association rule mining is available in
>> WEKA.
>> > > >>>>>>>>>
>> > > >>>>>>>>> Cheers,
>> > > >>>>>>>>> Eibe
>> > > >>>>>>>>>
>> > > >>>>>>>>>> On 2 Apr 2017, at 06:13, Bhupesh Rawat <[hidden email]>
>> wrote:
>> > > >>>>>>>>>>
>> > > >>>>>>>>>> Sir,
>> > > >>>>>>>>>> I have a boolean dataset which contains course enrollment
>> data
>> > > >>> which
>> > > >>>>>>>>>> means if a student has enrolled in a particular course
>> then it
>> > > >>>>>>>>>> is
>> > > >>>>>>>>>> indicated by 1 in the database else 0 is used. my question
>> is if
>> > > >>> it
>> > > >>>>>>>>>> is
>> > > >>>>>>>>>> possible to apply fuzzy association rule mining to this
>> data so
>> > > >>>>>>>>>> as
>> > > >>>>>>>>>> to
>> > > >>>>>>>>>> determine how interested(degree of interestingness)  a
>> student
>> > > >>>>>>>>>> is
>> > > >>> in
>> > > >>>>>>>>>> a
>> > > >>>>>>>>>> particular course.
>> > > >>>>>>>>>>
>> > > >>>>>>>>>> On 3/7/17, Eibe Frank <[hidden email]> wrote:
>> > > >>>>>>>>>>> That will depend on your application, more specifically,
>> the
>> > > >>>>>>>>>>> minimum
>> > > >>>>>>>>>>> accuracy that you want the rules to achieve and the
>> minimum
>> > > >>> amount
>> > > >>>>>>>>>>> of data
>> > > >>>>>>>>>>> that should support each rule.
>> > > >>>>>>>>>>>
>> > > >>>>>>>>>>> Note that Apriori is primarily a tool for exploratory
>> analysis
>> > > >>> and
>> > > >>>>>>>>>>> the main
>> > > >>>>>>>>>>> goal is normally to identify "interesting" rules.
>> > > >>>>>>>>>>> Interestingness
>> > > >>>>>>>>>>> may not be
>> > > >>>>>>>>>>> directly related to accuracy. Also, often, interesting
>> rules
>> > > >>>>>>>>>>> have
>> > > >>>>>>>>>>> quite
>> > > >>>>>>>>>>> limited support. Unfortunately, lowering the support to a
>> small
>> > > >>>>>>>>>>> value so
>> > > >>>>>>>>>>> that these rules can be captured can hugely increase the
>> total
>> > > >>>>>>>>>>> number of
>> > > >>>>>>>>>>> rules found and the runtime of the algorithm.
>> > > >>>>>>>>>>>
>> > > >>>>>>>>>>> Cheers,
>> > > >>>>>>>>>>> Eibe
>> > > >>>>>>>>>>>
>> > > >>>>>>>>>>>> On 7 Mar 2017, at 06:29, Bhupesh Rawat <[hidden email]
>> >
>> > > >>> wrote:
>> > > >>>>>>>>>>>>
>> > > >>>>>>>>>>>> What are the considerations that one should take into
>> account
>> > > >>>>>>>>>>>> while
>> > > >>>>>>>>>>>> setting the value of support and confidence in Apriori
>> > > >>> algorithm?
>> > > >>>>>>>>>>>>
>> > > >>>>>>>>>>>>
>> > > >>>>>>>>>>>>
>> > > >>>>>>>>>>>> On 3/6/17, Eibe Frank <[hidden email]> wrote:
>> > > >>>>>>>>>>>>> You should post this question in the appropriate help
>> forum
>> > > >>>>>>>>>>>>> for
>> > > >>>>>>>>>>>>> Matlab.
>> > > >>>>>>>>>>>>>
>> > > >>>>>>>>>>>>> Cheers,
>> > > >>>>>>>>>>>>> Eibe
>> > > >>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>> On 6/03/2017, at 7:14 AM, Bhupesh Rawat <
>> [hidden email]>
>> > > >>>>>>>>>>>>>> wrote:
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>> This question is related to the implementation of
>> Apriori in
>> > > >>>>>>>>>>>>>> MATLAB
>> > > >>>>>>>>>>>>>> which i have been trying to solve for quite some time
>> but
>> > > >>>>>>>>>>>>>> with
>> > > >>>>>>>>>>>>>> no
>> > > >>>>>>>>>>>>>> positive result. Any help would be highly appreciable.
>> I
>> > > >>>>>>>>>>>>>> have
>> > > >>>>>>>>>>>>>> attached
>> > > >>>>>>>>>>>>>> a file having two small dataset. the first dataset is
>> > > >>>>>>>>>>>>>> running
>> > > >>>>>>>>>>>>>> fine
>> > > >>>>>>>>>>>>>> with the Apriori algorithm, however the second dataset
>> > > >>>>>>>>>>>>>> almost
>> > > >>>>>>>>>>>>>> similar
>> > > >>>>>>>>>>>>>> to the first one except the last row, generates the
>> > > >>>>>>>>>>>>>> following
>> > > >>>>>>>>>>>>>> error:
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>> ??? Attempted to access count.%cell(16); index out of
>> bounds
>> > > >>>>>>>>>>>>>> because
>> > > >>>>>>>>>>>>>> numel(count.%cell)=15
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>> % Calculate Patterns Counts
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>    count{k+1}=zeros(size(C{2}));
>> > > >>>>>>>>>>>>>>    for r=1:numel(C{k+1})
>> > > >>>>>>>>>>>>>>        for i=1:numel(T)
>> > > >>>>>>>>>>>>>>            if IsContainedIn(C{k+1}{r},T{i})
>> > > >>>>>>>>>>>>>>                count{k+1}(r)=count{k+1}(r)+1;    %
>> line
>> > > >>>>>>>>>>>>>> containing the error
>> > > >>>>>>>>>>>>>>            end
>> > > >>>>>>>>>>>>>>        end
>> > > >>>>>>>>>>>>>>    end
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>> %% Apriori
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>> MST=0.2;   % Minimum Support Threshold
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>> MCT=0.2;    % Minimum Confidence Threshold
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>> [FinalRules, Rules]=Apriori(T,MST,MCT);    % line
>> containing
>> > > >>> the
>> > > >>>>>>>>>>>>>> error
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>> On 3/5/17, Eibe Frank <[hidden email]> wrote:
>> > > >>>>>>>>>>>>>>> You can create a new attribute by combining nominal
>> > > >>> attributes
>> > > >>>>>>>>>>>>>>> using
>> > > >>>>>>>>>>>>>>> the
>> > > >>>>>>>>>>>>>>> CartesianProduct filter.
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> Regarding the reliability of the rules, take a look
>> at the
>> > > >>>>>>>>>>>>>>> literature
>> > > >>>>>>>>>>>>>>> for
>> > > >>>>>>>>>>>>>>> "predictive apriori" on Google Scholar. I don't know
>> if
>> > > >>>>>>>>>>>>>>> there
>> > > >>>>>>>>>>>>>>> have been
>> > > >>>>>>>>>>>>>>> any
>> > > >>>>>>>>>>>>>>> extensive studies.
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> To get a rough idea of how well PredictiveApriori
>> works for
>> > > >>>>>>>>>>>>>>> your data,
>> > > >>>>>>>>>>>>>>> regardless of the accuracy of individual rules
>> considered
>> > > >>>>>>>>>>>>>>> in
>> > > >>>>>>>>>>>>>>> isolation,
>> > > >>>>>>>>>>>>>>> you
>> > > >>>>>>>>>>>>>>> could apply it to mine class association rules with
>> the
>> > > >>>>>>>>>>>>>>> JCBA
>> > > >>>>>>>>>>>>>>> classifier
>> > > >>>>>>>>>>>>>>> (from the classAssociationRules package) and use
>> > > >>>>>>>>>>>>>>> cross-validation for
>> > > >>>>>>>>>>>>>>> evaluation, similar to what we did in our paper.
>> Obviously,
>> > > >>> you
>> > > >>>>>>>>>>>>>>> will
>> > > >>>>>>>>>>>>>>> have
>> > > >>>>>>>>>>>>>>> to
>> > > >>>>>>>>>>>>>>> create an appropriate class attribute for each
>> > > >>>>>>>>>>>>>>> attribute/attribute
>> > > >>>>>>>>>>>>>>> combination that you are interested in (possibly using
>> > > >>>>>>>>>>>>>>> CartesianProduct).
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> Here is an example command-line, running JCBA with
>> > > >>>>>>>>>>>>>>> PredictiveApriori on
>> > > >>>>>>>>>>>>>>> the
>> > > >>>>>>>>>>>>>>> vote data (using the default class attribute). I got
>> it to
>> > > >>> only
>> > > >>>>>>>>>>>>>>> output
>> > > >>>>>>>>>>>>>>> the
>> > > >>>>>>>>>>>>>>> top two rules for simplicity:
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> ===================
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> java weka.Run .JCBA -A ".PredictiveApriori -N 2" -t
>> > > >>>>>>>>>>>>>>> ~/datasets/UCI/vote.arff
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> Options: -A ".PredictiveApriori -N 2"
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> Classification Rules (ordered):
>> > > >>>>>>>>>>>>>>> ==========================
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> 1.       physician-fee-freeze=n 3 0
>> > > >>>>>>>>>>>>>>> adoption-of-the-budget-resolution=y 2 1
>> > > >>>>>>>>>>>>>>> ==>
>> > > >>>>>>>>>>>>>>> Class=democrat     acc:(0.99),  (219),
>> > > >>>>>>>>>>>>>>> 2.       crime=n 13 0 el-salvador-aid=n 4 0
>> > > >>>>>>>>>>>>>>> adoption-of-the-budget-resolution=y
>> > > >>>>>>>>>>>>>>> 2
>> > > >>>>>>>>>>>>>>> 1  ==> Class=democrat     acc:(0.99),  (144),
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> Default Class: Class=republican
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> Additional Information:
>> > > >>>>>>>>>>>>>>> Number of Classification Associations Rules generated
>> by
>> > > >>>>>>>>>>>>>>> Rule
>> > > >>>>>>>>>>>>>>> Miner: 2
>> > > >>>>>>>>>>>>>>> Number of Classification Rules: 2
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> Mining Time in sec.: 7.867
>> > > >>>>>>>>>>>>>>> Pruning Time in sec. : 0.033
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> Time taken to build model: 7.91 seconds
>> > > >>>>>>>>>>>>>>> Time taken to test model on training data: 0.02
>> seconds
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> === Error on training data ===
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> Correctly Classified Instances         389
>> > > >>>>>>>>>>>>>>> 89.4253 %
>> > > >>>>>>>>>>>>>>> Incorrectly Classified Instances        46
>> > > >>>>>>>>>>>>>>> 10.5747 %
>> > > >>>>>>>>>>>>>>> Kappa statistic                          0.7877
>> > > >>>>>>>>>>>>>>> Mean absolute error                      0.1057
>> > > >>>>>>>>>>>>>>> Root mean squared error                  0.3252
>> > > >>>>>>>>>>>>>>> Relative absolute error                 22.2991 %
>> > > >>>>>>>>>>>>>>> Root relative squared error             66.7902 %
>> > > >>>>>>>>>>>>>>> Total Number of Instances              435
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> === Detailed Accuracy By Class ===
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>             TP Rate  FP Rate  Precision  Recall
>> > > >>>>>>>>>>>>>>> F-Measure
>> > > >>>>>>>>>>>>>>> MCC
>> > > >>>>>>>>>>>>>>> ROC Area  PRC Area  Class
>> > > >>>>>>>>>>>>>>>             0.828    0.000    1.000      0.828
>> 0.906
>> > > >>>>>>>>>>>>>>> 0.806
>> > > >>>>>>>>>>>>>>> 0.914     0.933     democrat
>> > > >>>>>>>>>>>>>>>             1.000    0.172    0.785      1.000
>> 0.880
>> > > >>>>>>>>>>>>>>> 0.806
>> > > >>>>>>>>>>>>>>> 0.914     0.785     republican
>> > > >>>>>>>>>>>>>>> Weighted Avg.    0.894    0.067    0.917      0.894
>> > > >>>>>>>>>>>>>>> 0.896
>> > > >>>>>>>>>>>>>>> 0.806
>> > > >>>>>>>>>>>>>>> 0.914     0.876
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> === Confusion Matrix ===
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> a   b   <-- classified as
>> > > >>>>>>>>>>>>>>> 221  46 |   a = democrat
>> > > >>>>>>>>>>>>>>> 0 168 |   b = republican
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> === Stratified cross-validation ===
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> Correctly Classified Instances         391
>> > > >>>>>>>>>>>>>>> 89.8851 %
>> > > >>>>>>>>>>>>>>> Incorrectly Classified Instances        44
>> > > >>>>>>>>>>>>>>> 10.1149 %
>> > > >>>>>>>>>>>>>>> Kappa statistic                          0.7957
>> > > >>>>>>>>>>>>>>> Mean absolute error                      0.1011
>> > > >>>>>>>>>>>>>>> Root mean squared error                  0.318
>> > > >>>>>>>>>>>>>>> Relative absolute error                 21.3284 %
>> > > >>>>>>>>>>>>>>> Root relative squared error             65.3201 %
>> > > >>>>>>>>>>>>>>> Total Number of Instances              435
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> === Detailed Accuracy By Class ===
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>             TP Rate  FP Rate  Precision  Recall
>> > > >>>>>>>>>>>>>>> F-Measure
>> > > >>>>>>>>>>>>>>> MCC
>> > > >>>>>>>>>>>>>>> ROC Area  PRC Area  Class
>> > > >>>>>>>>>>>>>>>             0.843    0.012    0.991      0.843
>> 0.911
>> > > >>>>>>>>>>>>>>> 0.810
>> > > >>>>>>>>>>>>>>> 0.915     0.932     democrat
>> > > >>>>>>>>>>>>>>>             0.988    0.157    0.798      0.988
>> 0.883
>> > > >>>>>>>>>>>>>>> 0.810
>> > > >>>>>>>>>>>>>>> 0.915     0.793     republican
>> > > >>>>>>>>>>>>>>> Weighted Avg.    0.899    0.068    0.917      0.899
>> > > >>>>>>>>>>>>>>> 0.900
>> > > >>>>>>>>>>>>>>> 0.810
>> > > >>>>>>>>>>>>>>> 0.915     0.878
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> === Confusion Matrix ===
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> a   b   <-- classified as
>> > > >>>>>>>>>>>>>>> 225  42 |   a = democrat
>> > > >>>>>>>>>>>>>>> 2 166 |   b = republican
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> ===================
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> The observed precision of classifications for class
>> > > >>>>>>>>>>>>>>> democrat
>> > > >>>>>>>>>>>>>>> estimated
>> > > >>>>>>>>>>>>>>> by
>> > > >>>>>>>>>>>>>>> cross-validation (under "Detailed Accuracy By Class")
>> is
>> > > >>> quite
>> > > >>>>>>>>>>>>>>> close to
>> > > >>>>>>>>>>>>>>> the
>> > > >>>>>>>>>>>>>>> accuracy estimates listed for the individual rules in
>> the
>> > > >>>>>>>>>>>>>>> initial
>> > > >>>>>>>>>>>>>>> output
>> > > >>>>>>>>>>>>>>> of
>> > > >>>>>>>>>>>>>>> the class association rules (0.99), so we can be
>> reasonably
>> > > >>>>>>>>>>>>>>> confident
>> > > >>>>>>>>>>>>>>> in
>> > > >>>>>>>>>>>>>>> this case that the rules are very accurate.
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> This process doesn't give you independent accuracy
>> > > >>>>>>>>>>>>>>> estimates
>> > > >>>>>>>>>>>>>>> for
>> > > >>>>>>>>>>>>>>> individual
>> > > >>>>>>>>>>>>>>> rules though. Assuming you have a reasonably large
>> test
>> > > >>>>>>>>>>>>>>> set,
>> > > >>>>>>>>>>>>>>> you could
>> > > >>>>>>>>>>>>>>> code
>> > > >>>>>>>>>>>>>>> up individual rules in PMML and use the PMML
>> classifier in
>> > > >>> WEKA
>> > > >>>>>>>>>>>>>>> for
>> > > >>>>>>>>>>>>>>> each
>> > > >>>>>>>>>>>>>>> rule to evaluate it on the test set
>> > > >>>>>>>>>>>>>>> (http://wiki.pentaho.com/display/DATAMINING/PMML+
>> > > >>> Support+in+Weka).
>> > > >>>>>>>>>>>>>>> However,
>> > > >>>>>>>>>>>>>>> you mentioned that you have a small dataset so this is
>> > > >>> probably
>> > > >>>>>>>>>>>>>>> not an
>> > > >>>>>>>>>>>>>>> option for you.
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> Cheers,
>> > > >>>>>>>>>>>>>>> Eibe
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>> On 5 Mar 2017, at 03:39, Bhupesh Rawat <
>> [hidden email]>
>> > > >>>>>>>>>>>>>>>> wrote:
>> > > >>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>> Sir,
>> > > >>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>> How to choose combination of attribute as a class
>> > > >>>>>>>>>>>>>>>> attribute
>> > > >>>>>>>>>>>>>>>> with Jrip
>> > > >>>>>>>>>>>>>>>> or PART in weka 3.8.
>> > > >>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>> Moreover i tried Predictive apriori on the dataset
>> and as
>> > > >>>>>>>>>>>>>>>> a
>> > > >>>>>>>>>>>>>>>> result i
>> > > >>>>>>>>>>>>>>>> found some rules with their respective accuracy. How
>> > > >>> reliable
>> > > >>>>>>>>>>>>>>>> are
>> > > >>>>>>>>>>>>>>>> those rules based on this accuracy.
>> > > >>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>> On Thu, Mar 2, 2017 at 2:10 AM, Eibe Frank
>> > > >>>>>>>>>>>>>>>> <[hidden email]> wrote:
>> > > >>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>> In WEKA 3.8/3.9, under
>> > > >>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>> filters.unsupervised.attribute.CartesianProduct
>> > > >>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>> Cheers,
>> > > >>>>>>>>>>>>>>>>> Eibe
>> > > >>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>> On 1/03/2017, at 6:13 PM, Bhupesh Rawat
>> > > >>>>>>>>>>>>>>>>>> <[hidden email]
>> > > >>>>
>> > > >>>>>>>>>>>>>>>>>> wrote:
>> > > >>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>> Thank you Sir, the problem has been fixed.
>> > > >>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>> Moreover i would also like to use the combination
>> of
>> > > >>>>>>>>>>>>>>>>>> attributes for
>> > > >>>>>>>>>>>>>>>>>> which  you suggested  the CartesionProduct filter.
>> Where
>> > > >>>>>>>>>>>>>>>>>> could i
>> > > >>>>>>>>>>>>>>>>>> find
>> > > >>>>>>>>>>>>>>>>>> this option?
>> > > >>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>> On 2/28/17, Eibe Frank <[hidden email]> wrote:
>> > > >>>>>>>>>>>>>>>>>>> What does the log say (see the ?log? tab next to
>> the
>> > > >>>>>>>>>>>>>>>>>>> ?status? tab)?
>> > > >>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>> Cheers,
>> > > >>>>>>>>>>>>>>>>>>> Eibe
>> > > >>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>> On 27/02/2017, at 11:56 PM, Bhupesh Rawat
>> > > >>>>>>>>>>>>>>>>>>>> <[hidden email]>
>> > > >>>>>>>>>>>>>>>>>>>> wrote:
>> > > >>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>> Sir,
>> > > >>>>>>>>>>>>>>>>>>>> When I use the KnowledgeFlow GUI the status
>> shown by
>> > > >>>>>>>>>>>>>>>>>>>> two
>> > > >>>>>>>>>>>>>>>>>>>> of the
>> > > >>>>>>>>>>>>>>>>> components
>> > > >>>>>>>>>>>>>>>>>>>> is interrupted(namely crossvalidationfoldmaker
>> and
>> > > >>>>>>>>>>>>>>>>>>>> J48)
>> > > >>> as
>> > > >>>>>>>>>>>>>>>>>>>> shown
>> > > >>>>>>>>>>>>>>>>>>>> in
>> > > >>>>>>>>>>>>>>>>>>>> the
>> > > >>>>>>>>>>>>>>>>>>>> attached file. How to fix it?
>> > > >>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>> On Mon, Feb 27, 2017 at 3:08 AM, Eibe Frank
>> > > >>>>>>>>>>>>>>>>>>>> <[hidden email]>
>> > > >>>>>>>>>>>>>>>>> wrote:
>> > > >>>>>>>>>>>>>>>>>>>> In the Explorer, there is no way to turn off
>> > > >>>>>>>>>>>>>>>>>>>> evaluation
>> > > >>>>>>>>>>>>>>>>>>>> completely.
>> > > >>>>>>>>>>>>>>>>>>>> You
>> > > >>>>>>>>>>>>>>>>>>>> could use the command-line interface or the
>> > > >>> KnowledgeFlow
>> > > >>>>>>>>>>>>>>>>>>>> GUI
>> > > >>>>>>>>>>>>>>>>>>>> though.
>> > > >>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>> Having said this, if you evaluate on the
>> training set,
>> > > >>> the
>> > > >>>>>>>>>>>>>>>>>>>> runtime
>> > > >>>>>>>>>>>>>>>>>>>> overhead is quite small if you apply a rule
>> learner.
>> > > >>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>> Note also that the Explorer always outputs the
>> > > >>>>>>>>>>>>>>>>>>>> classification
>> > > >>>>>>>>>>>>>>>>>>>> model
>> > > >>>>>>>>>>>>>>>>>>>> for
>> > > >>>>>>>>>>>>>>>>>>>> the *full* dataset loaded into the Preprocess
>> panel,
>> > > >>>>>>>>>>>>>>>>>>>> regardless of
>> > > >>>>>>>>>>>>>>>>> which
>> > > >>>>>>>>>>>>>>>>>>>> evaluation metric you choose, i.e., you will get
>> the
>> > > >>> rule
>> > > >>>>>>>>>>>>>>>>>>>> set for
>> > > >>>>>>>>>>>>>>>>>>>> the
>> > > >>>>>>>>>>>>>>>>> full
>> > > >>>>>>>>>>>>>>>>>>>> dataset regardless of the evaluation method you
>> use.
>> > > >>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>> Cheers,
>> > > >>>>>>>>>>>>>>>>>>>> Eibe
>> > > >>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>> On 26/02/2017, at 8:07 PM, Bhupesh Rawat
>> > > >>>>>>>>>>>>>>>>>>>>> <[hidden email]>
>> > > >>>>>>>>>>>>>>>>>>>>> wrote:
>> > > >>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>> Sir,
>> > > >>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>> How could i perform these two task
>> > > >>>>>>>>>>>>>>>>>>>>> seperately(applying
>> > > >>>>>>>>>>>>>>>>>>>>> classification
>> > > >>>>>>>>>>>>>>>>>>>>> rule learner and estimating classification
>> accuracy).
>> > > >>> The
>> > > >>>>>>>>>>>>>>>>>>>>> accuracy
>> > > >>>>>>>>>>>>>>>>>>>>> is
>> > > >>>>>>>>>>>>>>>>>>>>> estimated each time i run the classifier on the
>> > > >>> dataset.
>> > > >>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>> On 2/24/17, Eibe Frank <[hidden email]>
>> wrote:
>> > > >>>>>>>>>>>>>>>>>>>>>> No, not really. However, the dataset is quite
>> small.
>> > > >>> You
>> > > >>>>>>>>>>>>>>>>>>>>>> could
>> > > >>>>>>>>>>>>>>>>>>>>>> just
>> > > >>>>>>>>>>>>>>>>> run
>> > > >>>>>>>>>>>>>>>>>>>>>> a
>> > > >>>>>>>>>>>>>>>>>>>>>> classification rule learner such as JRip or
>> PART on
>> > > >>> the
>> > > >>>>>>>>>>>>>>>>>>>>>> data,
>> > > >>>>>>>>>>>>>>>>> treating
>> > > >>>>>>>>>>>>>>>>>>>>>> each
>> > > >>>>>>>>>>>>>>>>>>>>>> of the attributes in turn as the class
>> attribute.
>> > > >>>>>>>>>>>>>>>>>>>>>> Then
>> > > >>>>>>>>>>>>>>>>>>>>>> you can
>> > > >>>>>>>>>>>>>>>>> estimate
>> > > >>>>>>>>>>>>>>>>>>>>>> classification accuracy using cross-validation.
>> > > >>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>> You could also create combinations of
>> attributes
>> > > >>>>>>>>>>>>>>>>>>>>>> using
>> > > >>>>>>>>>>>>>>>>>>>>>> the
>> > > >>>>>>>>>>>>>>>>>>>>>> CartesionProduct
>> > > >>>>>>>>>>>>>>>>>>>>>> filter.
>> > > >>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>> Cheers,
>> > > >>>>>>>>>>>>>>>>>>>>>> Eibe
>> > > >>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>> On 24/02/2017, at 3:11 AM, Bhupesh Rawat
>> > > >>>>>>>>>>>>>>>>>>>>>>> <[hidden email]>
>> > > >>>>>>>>>>>>>>>>>>>>>>> wrote:
>> > > >>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>> I have a small dataset which contains student
>> > > >>> enrolment
>> > > >>>>>>>>>>>>>>>>>>>>>>> data in
>> > > >>>>>>>>>>>>>>>>>>>>>>> various courses. If a student has selected a
>> > > >>> particular
>> > > >>>>>>>>>>>>>>>>>>>>>>> course
>> > > >>>>>>>>>>>>>>>>>>>>>>> it
>> > > >>>>>>>>>>>>>>>>>>>>>>> is
>> > > >>>>>>>>>>>>>>>>>>>>>>> indicated by ?Y? else ?N? is used. I have also
>> > > >>> attached
>> > > >>>>>>>>>>>>>>>>>>>>>>> a file
>> > > >>>>>>>>>>>>>>>>>>>>>>> for
>> > > >>>>>>>>>>>>>>>>>>>>>>> better understanding of the dataset. I am
>> > > >>>>>>>>>>>>>>>>>>>>>>> interested
>> > > >>> in
>> > > >>>>>>>>>>>>>>>>>>>>>>> knowing
>> > > >>>>>>>>>>>>>>>>>>>>>>> if
>> > > >>>>>>>>>>>>>>>>> it
>> > > >>>>>>>>>>>>>>>>>>>>>>> is possible to measure the accuracy of the
>> > > >>> association
>> > > >>>>>>>>>>>>>>>>>>>>>>> rules
>> > > >>>>>>>>>>>>>>>>>>>>>>> with
>> > > >>>>>>>>>>>>>>>>> this
>> > > >>>>>>>>>>>>>>>>>>>>>>> dataset by the proposed approach in your
>> paper.
>> > > >>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>> On 2/23/17, Bhupesh Rawat <[hidden email]>
>> wrote:
>> > > >>>>>>>>>>>>>>>>>>>>>>>> Thank you so much for the response!!
>> > > >>>>>>>>>>>>>>>>>>>>>>>> On Feb 23, 2017 8:26 AM, "Eibe Frank"
>> > > >>>>>>>>>>>>>>>>>>>>>>>> <[hidden email]>
>> > > >>>>>>>>>>>>>>>>>>>>>>>> wrote:
>> > > >>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> You mean beyond confidence, lift, or one the
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> other
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> metrics
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> that
>> > > >>>>>>>>>>>>>>>>> you
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> can
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> get in the output of each rule? This is a
>> tough
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> question. One
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> way
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> may be
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> to
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> use the association rule mining algorithm to
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> build
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> classification
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> rules
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> and
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> then evaluate the accuracy of those
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> classification
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> rules. We
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> had
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> a
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> paper
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> on
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> this quite a while back:
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> Mutter, S., Hall, M., & Frank, E. (2004,
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> December).
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> Using
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> classification
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> to evaluate the output of confidence-based
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> association rule
>> > > >>>>>>>>>>>>>>>>> mining.
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> In
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> Australasian Joint Conference on Artificial
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> Intelligence (pp.
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> 538-549).
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> Springer Berlin Heidelberg.
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> I suppose you could also evaluate the
>> individual
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> association
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> rules
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> on a
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> separate test set, by computing the
>> confidence
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> measure, etc.,
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> on
>> > > >>>>>>>>>>>>>>>>> the
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> test
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> set for each rule, but this functionality
>> is not
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> provided by
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> WEKA.
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> Cheers,
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> Eibe
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> On 23/02/2017, at 12:46 AM, Bhupesh Rawat
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> <[hidden email]>
>> > > >>>>>>>>>>>>>>>>> wrote:
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Dear Sir/Madam
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> I have discovered some rules through weka.
>> Could
>> > > >>> you
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> tell me
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> how
>> > > >>>>>>>>>>>>>>>>> to
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> measure  the accuracy of those rules.
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> On Tue, Feb 14, 2017 at 3:44 AM, Peter
>> Reutemann
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> <[hidden email]>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> wrote:
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> The size of the final Apriori model as a
>> > > >>> serialised
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> Java
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> object
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> can
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> be
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> established saving it to a file and
>> considering
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> the
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> file
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> size.
>> > > >>>>>>>>>>>>>>>>> Note
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> that
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> this is different from the size of the
>> object in
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> memory (see,
>> > > >>>>>>>>>>>>>>>>> e.g.,
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> http://stackoverflow.com/
>> > > >>> questions/7146559/serialized-
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> object-size-vs-in-memory-
>> > > >>> object-size-in-java#7146941).
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> I don?t know of a good way to measure peak
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> memory
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> consumption
>> > > >>>>>>>>>>>>>>>>> of a
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> Java program (after garbage collection). A
>> crude
>> > > >>> way
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> would be
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> to
>> > > >>>>>>>>>>>>>>>>> run
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> the
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> program from the command-line (to avoid
>> overhead
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> associated
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> with
>> > > >>>>>>>>>>>>>>>>> the
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> GUIs)
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> with different maximum heap sizes, e.g.,
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> increasing
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> the heap
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> size
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> until
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> the
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> program runs through. Another option is to
>> look
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> at
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> the heap
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> size
>> > > >>>>>>>>>>>>>>>>> in
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> a
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> profiler (e.g., visualvm), enforcing garbage
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> collection
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> before
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> a
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> readout.
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> You can use the sizeofag javaagent for
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> determining
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> the size
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> of
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> a
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Java
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> object:
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> https://github.com/fracpete/sizeofag
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Credits to Maxim Zakharenkov, who wrote the
>> > > >>> original
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> code.
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Cheers, Peter
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> --
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Peter Reutemann
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Dept. of Computer Science
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> University of Waikato, NZ
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> +64 (7) 858-5174
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> http://www.cms.waikato.ac.nz/~fracpete/
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> http://www.data-mining.co.nz/
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> ______________________________
>> _________________
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> mailman/listinfo/wekalist
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> List etiquette:
>> http://www.cs.waikato.ac.nz/~
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> ml/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> --
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Thanks & Regards
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Bhupesh Rawat.
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Ph.D Scholar
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Department of Computer Science,Babasaheb
>> Bhimrao
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Ambedkar
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> University
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Ph. No: +91-9897065948
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> ..............................
>> > > >>> ..............................
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> ..............................
>> > > >>> .................................
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> *A man is the best judge of himself and he
>> has
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> to
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> pay the
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> price
>> > > >>>>>>>>>>>>>>>>> for
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> what
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> he
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> does.*
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> ..............................
>> > > >>> ..............................
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> ..............................
>> > > >>> .................................
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> ______________________________
>> _________________
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> mailman/listinfo/wekalist
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>> List etiquette:
>> http://www.cs.waikato.ac.nz/~
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> ml/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> ______________________________
>> _________________
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> mailman/listinfo/wekalist
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> List etiquette:
>> http://www.cs.waikato.ac.nz/~
>> > > >>>>>>>>>>>>>>>>>>>>>>>>> ml/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>> --
>> > > >>>>>>>>>>>>>>>>>>>>>>> Thanks & Regards
>> > > >>>>>>>>>>>>>>>>>>>>>>> Bhupesh Rawat.
>> > > >>>>>>>>>>>>>>>>>>>>>>> Ph.D Scholar
>> > > >>>>>>>>>>>>>>>>>>>>>>> Department of Computer Science,Babasaheb
>> Bhimrao
>> > > >>>>>>>>>>>>>>>>>>>>>>> Ambedkar
>> > > >>>>>>>>>>>>>>>>>>>>>>> University
>> > > >>>>>>>>>>>>>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > >>>>>>>>>>>>>>>>>>>>>>> Ph. No: +91-9897065948
>> > > >>>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>>> ..............................
>> > > >>> ..............................
>> > > >>>>>>>>>>>>>>>>> ..............................
>> > > >>> .................................
>> > > >>>>>>>>>>>>>>>>>>>>>>> *A man is the best judge of himself and he
>> has to
>> > > >>>>>>>>>>>>>>>>>>>>>>> pay
>> > > >>>>>>>>>>>>>>>>>>>>>>> the price
>> > > >>>>>>>>>>>>>>>>>>>>>>> for
>> > > >>>>>>>>>>>>>>>>>>>>>>> what
>> > > >>>>>>>>>>>>>>>>>>>>>>> he
>> > > >>>>>>>>>>>>>>>>>>>>>>> does.*
>> > > >>>>>>>>>>>>>>>>>>>>>>> ..............................
>> > > >>> ..............................
>> > > >>>>>>>>>>>>>>>>> ..............................
>> > > >>> .................................
>> > > >>>>>>>>>>>>>>>>>>>>>>> <students' data after
>> > > >>>>>>>>>>>>>>>>>>>>>>> preprocessin.xlsx>____________
>> > > >>> ___________________________________
>> > > >>>>>>>>>>>>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/mai
>> lman/listinfo/wekalist
>> > > >>>>>>>>>>>>>>>>>>>>>>> List etiquette:
>> > > >>>>>>>>>>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~
>> > > >>> ml/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>> ______________________________
>> _________________
>> > > >>>>>>>>>>>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/mai
>> lman/listinfo/wekalist
>> > > >>>>>>>>>>>>>>>>>>>>>> List etiquette:
>> > > >>>>>>>>>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~
>> > > >>> ml/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>> --
>> > > >>>>>>>>>>>>>>>>>>>>> Thanks & Regards
>> > > >>>>>>>>>>>>>>>>>>>>> Bhupesh Rawat.
>> > > >>>>>>>>>>>>>>>>>>>>> Ph.D Scholar
>> > > >>>>>>>>>>>>>>>>>>>>> Department of Computer Science,Babasaheb Bhimrao
>> > > >>> Ambedkar
>> > > >>>>>>>>>>>>>>>>>>>>> University
>> > > >>>>>>>>>>>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > >>>>>>>>>>>>>>>>>>>>> Ph. No: +91-9897065948
>> > > >>>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>> ..............................
>> > > >>> ..............................
>> > > >>>>>>>>>>>>>>>>> ..............................
>> > > >>> .................................
>> > > >>>>>>>>>>>>>>>>>>>>> *A man is the best judge of himself and he has
>> to pay
>> > > >>> the
>> > > >>>>>>>>>>>>>>>>>>>>> price
>> > > >>>>>>>>>>>>>>>>>>>>> for
>> > > >>>>>>>>>>>>>>>>> what
>> > > >>>>>>>>>>>>>>>>>>>>> he
>> > > >>>>>>>>>>>>>>>>>>>>> does.*
>> > > >>>>>>>>>>>>>>>>>>>>> ..............................
>> > > >>> ..............................
>> > > >>>>>>>>>>>>>>>>> ..............................
>> > > >>> .................................
>> > > >>>>>>>>>>>>>>>>>>>>> _______________________________________________
>> > > >>>>>>>>>>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/mai
>> lman/listinfo/wekalist
>> > > >>>>>>>>>>>>>>>>>>>>> List etiquette:
>> > > >>>>>>>>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~
>> > > >>> ml/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>> _______________________________________________
>> > > >>>>>>>>>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/mai
>> lman/listinfo/wekalist
>> > > >>>>>>>>>>>>>>>>>>>> List etiquette:
>> > > >>>>>>>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~
>> > > >>> ml/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>> --
>> > > >>>>>>>>>>>>>>>>>>>> Thanks & Regards
>> > > >>>>>>>>>>>>>>>>>>>> Bhupesh Rawat.
>> > > >>>>>>>>>>>>>>>>>>>> Ph.D Scholar
>> > > >>>>>>>>>>>>>>>>>>>> Department of Computer Science,Babasaheb Bhimrao
>> > > >>> Ambedkar
>> > > >>>>>>>>>>>>>>>>>>>> University
>> > > >>>>>>>>>>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > >>>>>>>>>>>>>>>>>>>> Ph. No: +91-9897065948
>> > > >>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>> ..............................
>> > > >>> ..............................
>> > > >>>>>>>>>>>>>>>>> ..............................
>> > > >>> .................................
>> > > >>>>>>>>>>>>>>>>>>>> *A man is the best judge of himself and he has
>> to pay
>> > > >>> the
>> > > >>>>>>>>>>>>>>>>>>>> price
>> > > >>>>>>>>>>>>>>>>>>>> for
>> > > >>>>>>>>>>>>>>>>> what
>> > > >>>>>>>>>>>>>>>>>>>> he
>> > > >>>>>>>>>>>>>>>>>>>> does.*
>> > > >>>>>>>>>>>>>>>>>>>> ..............................
>> > > >>> ..............................
>> > > >>>>>>>>>>>>>>>>> ..............................
>> > > >>> .................................
>> > > >>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>>> <knowledge flow
>> > > >>>>>>>>>>>>>>>>>>>> interuppted.docx>_____________
>> > > >>> __________________________________
>> > > >>>>>>>>>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/mai
>> lman/listinfo/wekalist
>> > > >>>>>>>>>>>>>>>>>>>> List etiquette:
>> > > >>>>>>>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~
>> > > >>> ml/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>> _______________________________________________
>> > > >>>>>>>>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/mai
>> lman/listinfo/wekalist
>> > > >>>>>>>>>>>>>>>>>>> List etiquette:
>> > > >>>>>>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~
>> > > >>> ml/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>> --
>> > > >>>>>>>>>>>>>>>>>> Thanks & Regards
>> > > >>>>>>>>>>>>>>>>>> Bhupesh Rawat.
>> > > >>>>>>>>>>>>>>>>>> Ph.D Scholar
>> > > >>>>>>>>>>>>>>>>>> Department of Computer Science,Babasaheb Bhimrao
>> > > >>>>>>>>>>>>>>>>>> Ambedkar
>> > > >>>>>>>>>>>>>>>>>> University
>> > > >>>>>>>>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > >>>>>>>>>>>>>>>>>> Ph. No: +91-9897065948
>> > > >>>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>>> ..............................
>> > > >>> ..............................
>> > > >>>>>>>>>>>>>>>>> ..............................
>> > > >>> .................................
>> > > >>>>>>>>>>>>>>>>>> *A man is the best judge of himself and he has to
>> pay
>> > > >>>>>>>>>>>>>>>>>> the
>> > > >>>>>>>>>>>>>>>>>> price for
>> > > >>>>>>>>>>>>>>>>>> what
>> > > >>>>>>>>>>>>>>>>> he
>> > > >>>>>>>>>>>>>>>>>> does.*
>> > > >>>>>>>>>>>>>>>>>> ..............................
>> > > >>> ..............................
>> > > >>>>>>>>>>>>>>>>> ..............................
>> > > >>> .................................
>> > > >>>>>>>>>>>>>>>>>> _______________________________________________
>> > > >>>>>>>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/
>> > > >>>>>>>>>>>>>>>>> mailman/listinfo/wekalist
>> > > >>>>>>>>>>>>>>>>>> List etiquette: http://www.cs.waikato.ac.nz/~
>> > > >>>>>>>>>>>>>>>>> ml/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>> _______________________________________________
>> > > >>>>>>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/
>> > > >>>>>>>>>>>>>>>>> mailman/listinfo/wekalist
>> > > >>>>>>>>>>>>>>>>> List etiquette: http://www.cs.waikato.ac.nz/~
>> > > >>>>>>>>>>>>>>>>> ml/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>> --
>> > > >>>>>>>>>>>>>>>> Thanks & Regards
>> > > >>>>>>>>>>>>>>>> Bhupesh Rawat.
>> > > >>>>>>>>>>>>>>>> Ph.D Scholar
>> > > >>>>>>>>>>>>>>>> Department of Computer Science,Babasaheb Bhimrao
>> Ambedkar
>> > > >>>>>>>>>>>>>>>> University
>> > > >>>>>>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > >>>>>>>>>>>>>>>> Ph. No: +91-9897065948
>> > > >>>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>>> ..............................
>> > > >>> ............................................................
>> > > >>> .................................
>> > > >>>>>>>>>>>>>>>> *A man is the best judge of himself and he has to
>> pay the
>> > > >>>>>>>>>>>>>>>> price for
>> > > >>>>>>>>>>>>>>>> what
>> > > >>>>>>>>>>>>>>>> he
>> > > >>>>>>>>>>>>>>>> does.*
>> > > >>>>>>>>>>>>>>>> ..............................
>> > > >>> ............................................................
>> > > >>> .................................
>> > > >>>>>>>>>>>>>>>> _______________________________________________
>> > > >>>>>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> > > >>>>>>>>>>>>>>>> List etiquette:
>> > > >>>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~m
>> l/weka/mailinglist_etiquette.
>> > > >>> html
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>> _______________________________________________
>> > > >>>>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> > > >>>>>>>>>>>>>>> List etiquette:
>> > > >>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~m
>> l/weka/mailinglist_etiquette.
>> > > >>> html
>> > > >>>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>> --
>> > > >>>>>>>>>>>>>> Thanks & Regards
>> > > >>>>>>>>>>>>>> Bhupesh Rawat.
>> > > >>>>>>>>>>>>>> Ph.D Scholar
>> > > >>>>>>>>>>>>>> Department of Computer Science,Babasaheb Bhimrao
>> Ambedkar
>> > > >>>>>>>>>>>>>> University
>> > > >>>>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > >>>>>>>>>>>>>> Ph. No: +91-9897065948
>> > > >>>>>>>>>>>>>>
>> > > >>>>>>>>>>>>>> ..............................
>> ..............................
>> > > >>> ...............................................................
>> > > >>>>>>>>>>>>>> *A man is the best judge of himself and he has to pay
>> the
>> > > >>> price
>> > > >>>>>>>>>>>>>> for what
>> > > >>>>>>>>>>>>>> he
>> > > >>>>>>>>>>>>>> does.*
>> > > >>>>>>>>>>>>>> ..............................
>> ..............................
>> > > >>> ...............................................................
>> > > >>>>>>>>>>>>>> <dataset_matlab.xls>__________
>> ______________________________
>> > > >>> _______
>> > > >>>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> > > >>>>>>>>>>>>>> List etiquette:
>> > > >>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~m
>> l/weka/mailinglist_etiquette.
>> > > >>> html
>> > > >>>>>>>>>>>>>
>> > > >>>>>>>>>>>>> _______________________________________________
>> > > >>>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> > > >>>>>>>>>>>>> List etiquette:
>> > > >>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~m
>> l/weka/mailinglist_etiquette.
>> > > >>> html
>> > > >>>>>>>>>>>>>
>> > > >>>>>>>>>>>>
>> > > >>>>>>>>>>>>
>> > > >>>>>>>>>>>> --
>> > > >>>>>>>>>>>> Thanks & Regards
>> > > >>>>>>>>>>>> Bhupesh Rawat.
>> > > >>>>>>>>>>>> Ph.D Scholar
>> > > >>>>>>>>>>>> Department of Computer Science,Babasaheb Bhimrao Ambedkar
>> > > >>>>>>>>>>>> University
>> > > >>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > >>>>>>>>>>>> Ph. No: +91-9897065948
>> > > >>>>>>>>>>>>
>> > > >>>>>>>>>>>> ..............................
>> ..............................
>> > > >>> ...............................................................
>> > > >>>>>>>>>>>> *A man is the best judge of himself and he has to pay the
>> > > >>>>>>>>>>>> price
>> > > >>>>>>>>>>>> for what
>> > > >>>>>>>>>>>> he
>> > > >>>>>>>>>>>> does.*
>> > > >>>>>>>>>>>> ..............................
>> ..............................
>> > > >>> ...............................................................
>> > > >>>>>>>>>>>> _______________________________________________
>> > > >>>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> > > >>>>>>>>>>>> List etiquette:
>> > > >>>>>>>>>>>> http://www.cs.waikato.ac.nz/~m
>> l/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>>>
>> > > >>>>>>>>>>> _______________________________________________
>> > > >>>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> > > >>>>>>>>>>> List etiquette:
>> > > >>>>>>>>>>> http://www.cs.waikato.ac.nz/~m
>> l/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>>>
>> > > >>>>>>>>>>
>> > > >>>>>>>>>>
>> > > >>>>>>>>>> --
>> > > >>>>>>>>>> Thanks & Regards
>> > > >>>>>>>>>> Bhupesh Rawat.
>> > > >>>>>>>>>> Ph.D Scholar
>> > > >>>>>>>>>> Department of Computer Science,Babasaheb Bhimrao Ambedkar
>> > > >>> University
>> > > >>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > >>>>>>>>>> Ph. No: +91-9897065948
>> > > >>>>>>>>>>
>> > > >>>>>>>>>> ..............................
>> ..............................
>> > > >>> ...............................................................
>> > > >>>>>>>>>> *A man is the best judge of himself and he has to pay the
>> price
>> > > >>> for
>> > > >>>>>>>>>> what he
>> > > >>>>>>>>>> does.*
>> > > >>>>>>>>>> ..............................
>> ..............................
>> > > >>> ...............................................................
>> > > >>>>>>>>>> _______________________________________________
>> > > >>>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>>> List info and subscription status:
>> > > >>>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> > > >>>>>>>>>> List etiquette:
>> > > >>>>>>>>>> http://www.cs.waikato.ac.nz/~m
>> l/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>
>> > > >>>>>>>>> _______________________________________________
>> > > >>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>> List info and subscription status:
>> > > >>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> > > >>>>>>>>> List etiquette:
>> > > >>>>>>>>> http://www.cs.waikato.ac.nz/~m
>> l/weka/mailinglist_etiquette.html
>> > > >>>>>>>>>
>> > > >>>>>>>>>
>> > > >>>>>>>>>
>> > > >>>>>>>>> --
>> > > >>>>>>>>> Thanks & Regards
>> > > >>>>>>>>> Bhupesh Rawat.
>> > > >>>>>>>>> Ph.D Scholar
>> > > >>>>>>>>> Department of Computer Science,Babasaheb Bhimrao Ambedkar
>> > > >>> University
>> > > >>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > >>>>>>>>> Ph. No: +91-9897065948
>> > > >>>>>>>>>
>> > > >>>>>>>>> ..............................
>> ..............................
>> > > >>> ...............................................................
>> > > >>>>>>>>> *A man is the best judge of himself and he has to pay the
>> price
>> > > >>>>>>>>> for
>> > > >>>>>>>>> what he
>> > > >>>>>>>>> does.*
>> > > >>>>>>>>> ..............................
>> ..............................
>> > > >>> ...............................................................
>> > > >>>>>>>>>
>> > > >>>>>>>>>
>> > > >>>>>>>>>
>> > > >>>>>>>>> _______________________________________________
>> > > >>>>>>>>> Wekalist mailing list
>> > > >>>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>>> List info and subscription status:
>> > > >>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> > > >>>>>>>>> List etiquette:
>> > > >>>>>>>>> http://www.cs.waikato.ac.nz/~m
>> l/weka/mailinglist_etiquette.html
>> > > >>>>>>>>
>> > > >>>>>>>> _______________________________________________
>> > > >>>>>>>> Wekalist mailing list
>> > > >>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>> List info and subscription status:
>> > > >>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> > > >>>>>>>> List etiquette:
>> > > >>>>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.h
>> tml
>> > > >>>>>>>>
>> > > >>>>>>>>
>> > > >>>>>>>>
>> > > >>>>>>>> --
>> > > >>>>>>>> Thanks & Regards
>> > > >>>>>>>> Bhupesh Rawat.
>> > > >>>>>>>> Ph.D Scholar
>> > > >>>>>>>> Department of Computer Science,Babasaheb Bhimrao Ambedkar
>> > > >>>>>>>> University
>> > > >>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > >>>>>>>> Ph. No: +91-9897065948
>> > > >>>>>>>>
>> > > >>>>>>>> ............................................................
>> > > >>> ...............................................................
>> > > >>>>>>>> *A man is the best judge of himself and he has to pay the
>> price
>> > > >>>>>>>> for
>> > > >>> what
>> > > >>>>>>>> he
>> > > >>>>>>>> does.*
>> > > >>>>>>>> ............................................................
>> > > >>> ...............................................................
>> > > >>>>>>>>
>> > > >>>>>>>>
>> > > >>>>>>>>
>> > > >>>>>>>> _______________________________________________
>> > > >>>>>>>> Wekalist mailing list
>> > > >>>>>>>> Send posts to: [hidden email]
>> > > >>>>>>>> List info and subscription status:
>> > > >>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> > > >>>>>>>> List etiquette:
>> > > >>>>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.h
>> tml
>> > > >>>>>>>
>> > > >>>>>>> _______________________________________________
>> > > >>>>>>> Wekalist mailing list
>> > > >>>>>>> Send posts to: [hidden email]
>> > > >>>>>>> List info and subscription status:
>> > > >>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> > > >>>>>>> List etiquette:
>> > > >>>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.h
>> tml
>> > > >>>>>>>
>> > > >>>>>>>
>> > > >>>>>>>
>> > > >>>>>>> --
>> > > >>>>>>> Thanks & Regards
>> > > >>>>>>> Bhupesh Rawat.
>> > > >>>>>>> Ph.D Scholar
>> > > >>>>>>> Department of Computer Science,Babasaheb Bhimrao Ambedkar
>> > > >>>>>>> University
>> > > >>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > >>>>>>> Ph. No: +91-9897065948
>> > > >>>>>>>
>> > > >>>>>>> ............................................................
>> > > >>> ...............................................................
>> > > >>>>>>> *A man is the best judge of himself and he has to pay the
>> price for
>> > > >>> what
>> > > >>>>>>> he
>> > > >>>>>>> does.*
>> > > >>>>>>> ............................................................
>> > > >>> ...............................................................
>> > > >>>>>>>
>> > > >>>>>>>
>> > > >>>>>>>
>> > > >>>>>>> _______________________________________________
>> > > >>>>>>> Wekalist mailing list
>> > > >>>>>>> Send posts to: [hidden email]
>> > > >>>>>>> List info and subscription status:
>> > > >>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> > > >>>>>>> List etiquette:
>> > > >>>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.h
>> tml
>> > > >>>>>>
>> > > >>>>>> _______________________________________________
>> > > >>>>>> Wekalist mailing list
>> > > >>>>>> Send posts to: [hidden email]
>> > > >>>>>> List info and subscription status:
>> > > >>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> > > >>>>>> List etiquette:
>> > > >>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.h
>> tml
>> > > >>>>>>
>> > > >>>>>
>> > > >>>>>
>> > > >>>>> --
>> > > >>>>> Thanks & Regards
>> > > >>>>> Bhupesh Rawat.
>> > > >>>>> Ph.D Scholar
>> > > >>>>> Department of Computer Science,Babasaheb Bhimrao Ambedkar
>> University
>> > > >>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > >>>>> Ph. No: +91-9897065948
>> > > >>>>>
>> > > >>>>> ............................................................
>> > > >>> ...............................................................
>> > > >>>>> *A man is the best judge of himself and he has to pay the price
>> for
>> > > >>> what he
>> > > >>>>> does.*
>> > > >>>>> ............................................................
>> > > >>> ...............................................................
>> > > >>>>> _______________________________________________
>> > > >>>>> Wekalist mailing list
>> > > >>>>> Send posts to: [hidden email]
>> > > >>>>> List info and subscription status: https://list.waikato.ac.nz/
>> > > >>> mailman/listinfo/wekalist
>> > > >>>>> List etiquette: http://www.cs.waikato.ac.nz/~
>> > > >>> ml/weka/mailinglist_etiquette.html
>> > > >>>>
>> > > >>>> _______________________________________________
>> > > >>>> Wekalist mailing list
>> > > >>>> Send posts to: [hidden email]
>> > > >>>> List info and subscription status: https://list.waikato.ac.nz/
>> > > >>> mailman/listinfo/wekalist
>> > > >>>> List etiquette: http://www.cs.waikato.ac.nz/~
>> > > >>> ml/weka/mailinglist_etiquette.html
>> > > >>>>
>> > > >>>>
>> > > >>>>
>> > > >>>> --
>> > > >>>> Thanks & Regards
>> > > >>>> Bhupesh Rawat.
>> > > >>>> Ph.D Scholar
>> > > >>>> Department of Computer Science,Babasaheb Bhimrao Ambedkar
>> University
>> > > >>>> Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > >>>> Ph. No: +91-9897065948
>> > > >>>>
>> > > >>>> ............................................................
>> > > >>> ...............................................................
>> > > >>>> *A man is the best judge of himself and he has to pay the price
>> for
>> > > >>>> what
>> > > >>> he
>> > > >>>> does.*
>> > > >>>> ............................................................
>> > > >>> ...............................................................
>> > > >>>>
>> > > >>>>
>> > > >>>>
>> > > >>>> _______________________________________________
>> > > >>>> Wekalist mailing list
>> > > >>>> Send posts to: [hidden email]
>> > > >>>> List info and subscription status: https://list.waikato.ac.nz/
>> > > >>> mailman/listinfo/wekalist
>> > > >>>> List etiquette: http://www.cs.waikato.ac.nz/~
>> > > >>> ml/weka/mailinglist_etiquette.html
>> > > >>>
>> > > >>> _______________________________________________
>> > > >>> Wekalist mailing list
>> > > >>> Send posts to: [hidden email]
>> > > >>> List info and subscription status: https://list.waikato.ac.nz/
>> > > >>> mailman/listinfo/wekalist
>> > > >>> List etiquette: http://www.cs.waikato.ac.nz/~
>> > > >>> ml/weka/mailinglist_etiquette.html
>> > > >>>
>> > > >>
>> > > >
>> > > >
>> > > > --
>> > > > Thanks & Regards
>> > > > Bhupesh Rawat.
>> > > > Ph.D Scholar
>> > > > Department of Computer Science,Babasaheb Bhimrao Ambedkar University
>> > > > Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > > Ph. No: +91-9897065948
>> > > >
>> > > > ............................................................
>> ...............................................................
>> > > > *A man is the best judge of himself and he has to pay the price for
>> what he
>> > > > does.*
>> > > > ............................................................
>> ...............................................................
>> > > > _______________________________________________
>> > > > Wekalist mailing list
>> > > > Send posts to: [hidden email]
>> > > > List info and subscription status: https://list.waikato.ac.nz/mai
>> lman/listinfo/wekalist
>> > > > List etiquette: http://www.cs.waikato.ac.nz/~m
>> l/weka/mailinglist_etiquette.html
>> > >
>> > > _______________________________________________
>> > > Wekalist mailing list
>> > > Send posts to: [hidden email]
>> > > List info and subscription status: https://list.waikato.ac.nz/mai
>> lman/listinfo/wekalist
>> > > List etiquette: http://www.cs.waikato.ac.nz/~m
>> l/weka/mailinglist_etiquette.html
>> > >
>> > >
>> > >
>> > > --
>> > > Thanks & Regards
>> > > Bhupesh Rawat.
>> > > Ph.D Scholar
>> > > Department of Computer Science,Babasaheb Bhimrao Ambedkar University
>> > > Vidya Vihar,Rai Bareilly road(Lucknow)
>> > > Ph. No: +91-9897065948
>> > >
>> > > ............................................................
>> ...............................................................
>> > > *A man is the best judge of himself and he has to pay the price for
>> what he
>> > > does.*
>> > > ............................................................
>> ...............................................................
>> > >
>> > >
>> > >
>> > > _______________________________________________
>> > > Wekalist mailing list
>> > > Send posts to: [hidden email]
>> > > List info and subscription status: https://list.waikato.ac.nz/mai
>> lman/listinfo/wekalist
>> > > List etiquette: http://www.cs.waikato.ac.nz/~m
>> l/weka/mailinglist_etiquette.html
>> >
>> > _______________________________________________
>> > Wekalist mailing list
>> > Send posts to: [hidden email]
>> > List info and subscription status: https://list.waikato.ac.nz/mai
>> lman/listinfo/wekalist
>> > List etiquette: http://www.cs.waikato.ac.nz/~m
>> l/weka/mailinglist_etiquette.html
>> >
>> >
>> >
>> > --
>> > Thanks & Regards
>> > Bhupesh Rawat.
>> > Ph.D Scholar
>> > Department of Computer Science,Babasaheb Bhimrao Ambedkar University
>> > Vidya Vihar,Rai Bareilly road(Lucknow)
>> > Ph. No: +91-9897065948
>> >
>> > ............................................................
>> ...............................................................
>> > *A man is the best judge of himself and he has to pay the price for
>> what he
>> > does.*
>> > ............................................................
>> ...............................................................
>> >
>> >
>> >
>> > _______________________________________________
>> > Wekalist mailing list
>> > Send posts to: [hidden email]
>> > List info and subscription status: https://list.waikato.ac.nz/mai
>> lman/listinfo/wekalist
>> > List etiquette: http://www.cs.waikato.ac.nz/~m
>> l/weka/mailinglist_etiquette.html
>>
>> _______________________________________________
>> Wekalist mailing list
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>> l/weka/mailinglist_etiquette.html
>>
>
>
>
> --
> Thanks & Regards
> Bhupesh Rawat.
> Ph.D Scholar
> Department of Computer Science,Babasaheb Bhimrao Ambedkar University
> Vidya Vihar,Rai Bareilly road(Lucknow)
> Ph. No: +91-9897065948
>
> ............................................................
> ...............................................................
> *A man is the best judge of himself and he has to pay the price for what
> he
> does.*
> ............................................................
> ...............................................................
>
>
>
>


--
Thanks & Regards
Bhupesh Rawat.
Ph.D Scholar
Department of Computer Science,Babasaheb Bhimrao Ambedkar University
Vidya Vihar,Rai Bareilly road(Lucknow)
Ph. No: +91-9897065948

...........................................................................................................................
*A man is the best judge of himself and he has to pay the price for what he
does.*
...........................................................................................................................
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Re: Wekalist Digest, Vol 172, Issue 44

Eibe Frank-2
Administrator
The corresponding training data will have 3 classes and 22 classes respectively, so the classification boundaries learned from the training data will be different even if you only consider the 3 classes present in both sets (one exception would be ZeroR, which predicts the majority class for every point in the instance space; the majority class could be the same in both cases).

So the question to your answer is no, you will generally not get the same classifications.

Cheers,
Eibe

PS: Please don’t respond to the digest. Use the Nabble mailing list interface if necessary.

> On 14/06/2017, at 10:33 AM, Michael Calve <[hidden email]> wrote:
>
> In my test set can be up to 22 different wifi devices, of which I am capturing network traffic data on, using a WiFi sniffer.  Their class attributes are labeled as question marks in order for weka to classify them.
>
> My question pertains to whether or not the testing accuracy decreases or increases depending on how many of these devices' data (250 instances per device and three attributes per instance) are predicted at one time.  Basically, if I create two test sets, one with 3 devices (750 total instances), and one with 22 devices (5,500 instances), will I get the same results for the three devices that are in both of these test sets???
>
> On Tue, Jun 13, 2017 at 7:45 AM, <[hidden email]> wrote:
> Send Wekalist mailing list submissions to
>         [hidden email]
>
> To subscribe or unsubscribe via the World Wide Web, visit
>         https://list.waikato.ac.nz/mailman/listinfo/wekalist
> or, via email, send a message with subject or body 'help' to
>         [hidden email]
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>         [hidden email]
>
> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of Wekalist digest..."
>
>
> Today's Topics:
>
>    1. Re: Does Amount of Test Classes matter for Binary Random
>       Forest Classifier (Eibe Frank)
>    2. Look into a model (Thomas Pfau)
>    3. Explanation of misclassification (Alexander Osherenko)
>    4. how to load a class from an installed package in java     code
>       (Ignacio Arganda-Carreras)
>    5. Re: Measuring accuracy and efficiency of association      rules!
>       (Bhupesh Rawat)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Tue, 13 Jun 2017 16:37:37 +1200
> From: Eibe Frank <[hidden email]>
> To: "Weka machine learning workbench list."
>         <[hidden email]>
> Subject: Re: [Wekalist] Does Amount of Test Classes matter for Binary
>         Random  Forest Classifier
> Message-ID: <[hidden email]>
> Content-Type: text/plain; charset=utf-8
>
> I think you?ll have to explain more precisely what you are doing. Can you give a step by step description?
>
> Cheers,
> Eibe
>
> > On 13/06/2017, at 8:25 AM, Michael Calve <[hidden email]> wrote:
> >
> > Hello,
> >
> > I am curious if there is a cap for the number of classes can be classified/predicted simulatenously using a Binary Random Forest?
> >
> > I've currently classifying 22 classes at a time, with 250 instances per class and 3 attributes per instance.  I then use a threshold to decide, based upon the number of instances out of 250, that class has been predicted as.  I use this Binary Random Forest as a filter and then whichever ones pass the threshold, get placed into a testing file for a 6 class Multi Random Forest Classification.
> >
> > Basically, does the number of classes in the testing set matter?  For both a Binary Random Forest, and a MultiClass Random Forest?
> >
> > Thanks!
> > Michael
> > _______________________________________________
> > Wekalist mailing list
> > Send posts to: [hidden email]
> > List info and subscription status: https://list.waikato.ac.nz/mailman/listinfo/wekalist
> > List etiquette: http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
>
>
>
> ------------------------------
>
> Message: 2
> Date: Tue, 13 Jun 2017 07:40:59 +0200
> From: Thomas Pfau <[hidden email]>
> To: <[hidden email]>
> Subject: [Wekalist] Look into a model
> Message-ID: <[hidden email]>
> Content-Type: text/plain; charset="utf-8"
>
> Hi,
>
> I have a model represented by a voter combination of multiple random
> forests. I'm wondering, whether there is any way to actually have a look
> at the individual random forests i.e. see what the forests are
> doing/which decisions they make.
>
> Best
>
> Thomas
>
> --
> Universit? du Luxembourg
> Facult? des Sciences, de la Technologie et de la Communication
> Campus Belval, Biotech II 115
> 6 avenue du Swing
> L-4367 Belvaux
> Tel: (+352) 46 66 44 5309
> Email: [hidden email]
>
>
>
> ------------------------------
>
> Message: 3
> Date: Tue, 13 Jun 2017 00:34:41 -0700 (MST)
> From: Alexander Osherenko <[hidden email]>
> To: [hidden email]
> Subject: [Wekalist] Explanation of misclassification
> Message-ID: <[hidden email]>
> Content-Type: text/plain; charset=UTF-8
>
> I wonder: are there some articles that aim at the ?error explanation?? of
> classification results that consider ?the chosen classifier, the data or
> some other aspects and explain the probable reason of misclassification? For
> example, a typical answer of this question would be "a classifier works not
> good with sparse data" or "a classifier works not good because of data
> overfitting".
>
> Best, Alexander
>
>
>
> --
> View this message in context: http://weka.8497.n7.nabble.com/Explanation-of-misclassification-tp40936.html
> Sent from the WEKA mailing list archive at Nabble.com.
>
>
> ------------------------------
>
> Message: 4
> Date: Tue, 13 Jun 2017 12:14:38 +0200
> From: Ignacio Arganda-Carreras <[hidden email]>
> To: "Weka machine learning workbench list."
>         <[hidden email]>
> Subject: [Wekalist] how to load a class from an installed package in
>         java    code
> Message-ID:
>         <CAE=[hidden email]>
> Content-Type: text/plain; charset="utf-8"
>
> Dear all,
>
> I have installed the ClassificationViaClustering classifier using the
> package manager on my Weka 3.9.1 and now I would like to instantiate it
> from java code. So far I have not been successful and I can only load the
> classifier classes that come by default in the weka jar.
>
> This is what I tried:
>
> import weka.core.WekaPackageManager;
>
> WekaPackageManager.loadPackages( true );
>
> Which outputs:
>
> WARNING: core mtj jar files are not available as resources to this
> classloader (sun.misc.Launcher$AppClassLoader@764c12b6)
> [WekaPackageManager] loading package collective-classification
> [WekaPackageManager] loading package classificationViaClustering
> Registering weka.classifiers.collective.util.Flipper
> weka.gui.GenericObjectEditor
> Refreshing GOE props...
>
> So the package seems to be loaded, but then, when I try to instantiate it,
> I get a "class not found error":
>
> ClassificationViaClustering classifier = new ClassificationViaClustering();
>
> What am I missing?
>
> Thanks a lot in advance!
>
> ignacio
>
>
> --
> Ignacio Arganda-Carreras, Ph.D.
> Ikerbasque Research Fellow
> Departamento de Ciencia de la Computacion e Inteligencia Artificial
> Facultad de Informatica, Universidad del Pais Vasco
> Paseo de Manuel Lardizabal, 1
> 20018 Donostia-San Sebastian
> Guipuzcoa, Spain
>
> Phone : +34 943 01 73 25
> Website: http://sites.google.com/site/iargandacarreras/
> <http://biocomp.cnb.csic.es/%7Eiarganda/index_EN.html>
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>
> ------------------------------
>
> Message: 5
> Date: Tue, 13 Jun 2017 08:47:54 +0530
> From: Bhupesh Rawat <[hidden email]>
> To: "Weka machine learning workbench list."
>         <[hidden email]>
> Subject: Re: [Wekalist] Measuring accuracy and efficiency of
>         association     rules!
> Message-ID:
>         <CAMa3TT1HS=ik+zq5zxAyuxZGWF=[hidden email]>
> Content-Type: text/plain; charset="utf-8"
>
> Hi!
>
> I am trying to run predictive apriori algorithm using command line
> interface in weka. can you help me with the options of this algorithm.
>
> On Wed, May 31, 2017 at 2:54 PM, Bhupesh Rawat <[hidden email]> wrote:
>
> > Thank your Sir! it worked.
> >
> > On Wed, May 31, 2017 at 2:47 PM, Eibe Frank <[hidden email]> wrote:
> >
> >> Yes, the SimpleCLI will also output the elapsed time. Here is how it is
> >> measured:
> >>
> >> startTime = System.currentTimeMillis();
> >> associator.buildAssociations(data);
> >> endTime = System.currentTimeMillis();
> >>
> >> Space consumption at runtime cannot easily be measured in WEKA.
> >>
> >> Cheers,
> >> Eibe
> >>
> >> > On 30 May 2017, at 14:18, Bhupesh Rawat <[hidden email]> wrote:
> >> >
> >> > I want to compare the performance of apriori,FP growth and Tertius in
> >> terms of time and space within weka. moreover what is the use of elapsed
> >> time? how it is different from execution time? will smple CLI help to
> >> achieve my goal?
> >> >
> >> > On Mon, May 29, 2017 at 11:25 AM, Eibe Frank <[hidden email]>
> >> wrote:
> >> > The elapsed time is shown in the output when you run an association
> >> rule learner in WEKA from the command-line interface.
> >> >
> >> > If you want to compare Apriori and PredictiveApriori, which generate
> >> different sets of rules, you can use classification based on association
> >> rule mining to measure predictive accuracy by installing the
> >> classAssociationRules package. This approach was used in
> >> >
> >> >   Stefan Mutter, Mark Hall, and Eibe Frank. Using classification to
> >> evaluate the output of confidence-based association rule mining. In Proc
> >> 17th Australian Joint Conference on Artificial Intelligence, Cairns,
> >> Australia, pages 538--549. Springer, 2004.
> >> >
> >> > WEKA?s native implementations of frequent pattern mining algorithms are
> >> few in number. You might also want to take a look at the SPMFWrapper
> >> package for WEKA.
> >> >
> >> > Cheers,
> >> > Eibe
> >> >
> >> > > On 29/05/2017, at 2:30 PM, Bhupesh Rawat <[hidden email]> wrote:
> >> > >
> >> > > could you explain with an example, how to find the running time of an
> >> algorithm? moreover I am trying to compare the performance of association
> >> rule mining algorithms, could you suggest me some parameters for comparison
> >> apart from running time and space.
> >> > >
> >> > > On Mon, May 29, 2017 at 4:10 AM, Eibe Frank <[hidden email]>
> >> wrote:
> >> > > It should have some effect on runtime.
> >> > >
> >> > > I assume you are talking about the number of instances in the data?
> >> For Apriori, the number of attributes is often more important, because it
> >> determines the number of item set candidates that are available.
> >> > >
> >> > > Cheers,
> >> > > Eibe
> >> > >
> >> > > > On 29/05/2017, at 1:27 AM, Bhupesh Rawat <[hidden email]> wrote:
> >> > > >
> >> > > > Hello Weka Users!
> >> > > >
> >> > > > Although I increased the data size from 50 to 10000,it has no effect
> >> > > > on the running time(Apriori algorithm). why?
> >> > > >
> >> > > > On 4/20/17, Bhupesh Rawat <[hidden email]> wrote:
> >> > > >> Thank you Sir!
> >> > > >> On Apr 20, 2017 3:52 PM, "Eibe Frank" <[hidden email]> wrote:
> >> > > >>
> >> > > >>> Because those algorithm implementations are not able to deal with
> >> the
> >> > > >>> particular data you have loaded. For example, the FPGrowth
> >> implementation
> >> > > >>> cannot deal with nominal attributes that have more than two
> >> values. You
> >> > > >>> can
> >> > > >>> click the "Capabilities" button in the "GenericObjectEditor" for a
> >> > > >>> particular associator to see what kind of data it can deal with.
> >> > > >>>
> >> > > >>> Cheers,
> >> > > >>> Eibe
> >> > > >>>
> >> > > >>>> On 19 Apr 2017, at 19:02, Bhupesh Rawat <[hidden email]>
> >> wrote:
> >> > > >>>>
> >> > > >>>> I am using weka 3.8.1. Why some association rules algorithms are
> >> > > >>> disabled when i apply them to the dataset.
> >> > > >>>>
> >> > > >>>> On Wed, Apr 19, 2017 at 4:03 AM, Eibe Frank <[hidden email]>
> >> wrote:
> >> > > >>>> You will be able to see, for example, which words co-occur
> >> frequently.
> >> > > >>>>
> >> > > >>>> Cheers,
> >> > > >>>> Eibe
> >> > > >>>>
> >> > > >>>>> On 19/04/2017, at 5:46 AM, Bhupesh Rawat <[hidden email]>
> >> wrote:
> >> > > >>>>>
> >> > > >>>>> What kind of possible useful results can be found after
> >> > > >>>>> experimentally
> >> > > >>>>> comparing  various association rules mining algorithms(apriori,
> >> > > >>>>> tertius  and predictive) to the e-learning data.
> >> > > >>>>>
> >> > > >>>>> On 4/11/17, Eibe Frank <[hidden email]> wrote:
> >> > > >>>>>> You could create a dataset with a single string attribute
> >> holding
> >> > > >>>>>> the
> >> > > >>> text
> >> > > >>>>>> of each web page, apply the StringToWordVector filter,
> >> followed by
> >> > > >>>>>> NumericToNominal, and then apply Apriori.
> >> > > >>>>>>
> >> > > >>>>>> Cheers,
> >> > > >>>>>> Eibe
> >> > > >>>>>>
> >> > > >>>>>>> On 10/04/2017, at 11:11 PM, Bhupesh Rawat <[hidden email]>
> >> wrote:
> >> > > >>>>>>>
> >> > > >>>>>>> How to use the string dataset for the task of information
> >> > > >>>>>>> extraction
> >> > > >>> from
> >> > > >>>>>>> web pages with Apriori algorithm in Weka?
> >> > > >>>>>>>
> >> > > >>>>>>> On Sat, Apr 8, 2017 at 4:41 PM, Bhupesh Rawat <
> >> [hidden email]>
> >> > > >>> wrote:
> >> > > >>>>>>> Thanks Eibe.
> >> > > >>>>>>>
> >> > > >>>>>>> On Apr 8, 2017 2:57 PM, "Eibe Frank" <[hidden email]>
> >> wrote:
> >> > > >>>>>>> It's available in a separate package:
> >> > > >>>>>>>
> >> > > >>>>>>> http://weka.sourceforge.net/packageMetaData/tertius/index.ht
> >> ml
> >> > > >>>>>>>
> >> > > >>>>>>> You can install this package with the WEKA package manager.
> >> > > >>>>>>>
> >> > > >>>>>>> Cheers,
> >> > > >>>>>>> Eibe
> >> > > >>>>>>>
> >> > > >>>>>>>> On 8 Apr 2017, at 19:41, Bhupesh Rawat <[hidden email]>
> >> wrote:
> >> > > >>>>>>>>
> >> > > >>>>>>>> Where can i find the Tertitus algorithm(association rule
> >> mining)
> >> > > >>>>>>>> in
> >> > > >>> weka
> >> > > >>>>>>>> 3.8.1.
> >> > > >>>>>>>>
> >> > > >>>>>>>> On Sun, Apr 2, 2017 at 9:02 AM, Eibe Frank <
> >> [hidden email]>
> >> > > >>> wrote:
> >> > > >>>>>>>> Possibly, I'm not familiar with those methods. However, if
> >> you
> >> > > >>>>>>>> have
> >> > > >>>>>>>> Boolean data, you might just want to apply a conventional
> >> > > >>> association
> >> > > >>>>>>>> rule miner anyway.
> >> > > >>>>>>>>
> >> > > >>>>>>>> Cheers,
> >> > > >>>>>>>> Eibe
> >> > > >>>>>>>>
> >> > > >>>>>>>>> On 2 Apr 2017, at 14:54, Bhupesh Rawat <[hidden email]>
> >> wrote:
> >> > > >>>>>>>>>
> >> > > >>>>>>>>> Is it possible to apply fuzzy logic to boolean data?
> >> > > >>>>>>>>>
> >> > > >>>>>>>>> On Sun, Apr 2, 2017 at 5:32 AM, Eibe Frank <
> >> [hidden email]>
> >> > > >>> wrote:
> >> > > >>>>>>>>> I would treat this as a classification problem and build a
> >> > > >>> per-course
> >> > > >>>>>>>>> model that estimates the probability that a student will
> >> take a
> >> > > >>>>>>>>> particular course given that they have taken a particular
> >> set of
> >> > > >>>>>>>>> courses already.
> >> > > >>>>>>>>>
> >> > > >>>>>>>>> I don't think fuzzy association rule mining is available in
> >> WEKA.
> >> > > >>>>>>>>>
> >> > > >>>>>>>>> Cheers,
> >> > > >>>>>>>>> Eibe
> >> > > >>>>>>>>>
> >> > > >>>>>>>>>> On 2 Apr 2017, at 06:13, Bhupesh Rawat <[hidden email]>
> >> wrote:
> >> > > >>>>>>>>>>
> >> > > >>>>>>>>>> Sir,
> >> > > >>>>>>>>>> I have a boolean dataset which contains course enrollment
> >> data
> >> > > >>> which
> >> > > >>>>>>>>>> means if a student has enrolled in a particular course
> >> then it
> >> > > >>>>>>>>>> is
> >> > > >>>>>>>>>> indicated by 1 in the database else 0 is used. my question
> >> is if
> >> > > >>> it
> >> > > >>>>>>>>>> is
> >> > > >>>>>>>>>> possible to apply fuzzy association rule mining to this
> >> data so
> >> > > >>>>>>>>>> as
> >> > > >>>>>>>>>> to
> >> > > >>>>>>>>>> determine how interested(degree of interestingness)  a
> >> student
> >> > > >>>>>>>>>> is
> >> > > >>> in
> >> > > >>>>>>>>>> a
> >> > > >>>>>>>>>> particular course.
> >> > > >>>>>>>>>>
> >> > > >>>>>>>>>> On 3/7/17, Eibe Frank <[hidden email]> wrote:
> >> > > >>>>>>>>>>> That will depend on your application, more specifically,
> >> the
> >> > > >>>>>>>>>>> minimum
> >> > > >>>>>>>>>>> accuracy that you want the rules to achieve and the
> >> minimum
> >> > > >>> amount
> >> > > >>>>>>>>>>> of data
> >> > > >>>>>>>>>>> that should support each rule.
> >> > > >>>>>>>>>>>
> >> > > >>>>>>>>>>> Note that Apriori is primarily a tool for exploratory
> >> analysis
> >> > > >>> and
> >> > > >>>>>>>>>>> the main
> >> > > >>>>>>>>>>> goal is normally to identify "interesting" rules.
> >> > > >>>>>>>>>>> Interestingness
> >> > > >>>>>>>>>>> may not be
> >> > > >>>>>>>>>>> directly related to accuracy. Also, often, interesting
> >> rules
> >> > > >>>>>>>>>>> have
> >> > > >>>>>>>>>>> quite
> >> > > >>>>>>>>>>> limited support. Unfortunately, lowering the support to a
> >> small
> >> > > >>>>>>>>>>> value so
> >> > > >>>>>>>>>>> that these rules can be captured can hugely increase the
> >> total
> >> > > >>>>>>>>>>> number of
> >> > > >>>>>>>>>>> rules found and the runtime of the algorithm.
> >> > > >>>>>>>>>>>
> >> > > >>>>>>>>>>> Cheers,
> >> > > >>>>>>>>>>> Eibe
> >> > > >>>>>>>>>>>
> >> > > >>>>>>>>>>>> On 7 Mar 2017, at 06:29, Bhupesh Rawat <[hidden email]
> >> >
> >> > > >>> wrote:
> >> > > >>>>>>>>>>>>
> >> > > >>>>>>>>>>>> What are the considerations that one should take into
> >> account
> >> > > >>>>>>>>>>>> while
> >> > > >>>>>>>>>>>> setting the value of support and confidence in Apriori
> >> > > >>> algorithm?
> >> > > >>>>>>>>>>>>
> >> > > >>>>>>>>>>>>
> >> > > >>>>>>>>>>>>
> >> > > >>>>>>>>>>>> On 3/6/17, Eibe Frank <[hidden email]> wrote:
> >> > > >>>>>>>>>>>>> You should post this question in the appropriate help
> >> forum
> >> > > >>>>>>>>>>>>> for
> >> > > >>>>>>>>>>>>> Matlab.
> >> > > >>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>> Cheers,
> >> > > >>>>>>>>>>>>> Eibe
> >> > > >>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>> On 6/03/2017, at 7:14 AM, Bhupesh Rawat <
> >> [hidden email]>
> >> > > >>>>>>>>>>>>>> wrote:
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>> This question is related to the implementation of
> >> Apriori in
> >> > > >>>>>>>>>>>>>> MATLAB
> >> > > >>>>>>>>>>>>>> which i have been trying to solve for quite some time
> >> but
> >> > > >>>>>>>>>>>>>> with
> >> > > >>>>>>>>>>>>>> no
> >> > > >>>>>>>>>>>>>> positive result. Any help would be highly appreciable.
> >> I
> >> > > >>>>>>>>>>>>>> have
> >> > > >>>>>>>>>>>>>> attached
> >> > > >>>>>>>>>>>>>> a file having two small dataset. the first dataset is
> >> > > >>>>>>>>>>>>>> running
> >> > > >>>>>>>>>>>>>> fine
> >> > > >>>>>>>>>>>>>> with the Apriori algorithm, however the second dataset
> >> > > >>>>>>>>>>>>>> almost
> >> > > >>>>>>>>>>>>>> similar
> >> > > >>>>>>>>>>>>>> to the first one except the last row, generates the
> >> > > >>>>>>>>>>>>>> following
> >> > > >>>>>>>>>>>>>> error:
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>> ??? Attempted to access count.%cell(16); index out of
> >> bounds
> >> > > >>>>>>>>>>>>>> because
> >> > > >>>>>>>>>>>>>> numel(count.%cell)=15
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>> % Calculate Patterns Counts
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>    count{k+1}=zeros(size(C{2}));
> >> > > >>>>>>>>>>>>>>    for r=1:numel(C{k+1})
> >> > > >>>>>>>>>>>>>>        for i=1:numel(T)
> >> > > >>>>>>>>>>>>>>            if IsContainedIn(C{k+1}{r},T{i})
> >> > > >>>>>>>>>>>>>>                count{k+1}(r)=count{k+1}(r)+1;    %
> >> line
> >> > > >>>>>>>>>>>>>> containing the error
> >> > > >>>>>>>>>>>>>>            end
> >> > > >>>>>>>>>>>>>>        end
> >> > > >>>>>>>>>>>>>>    end
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>> %% Apriori
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>> MST=0.2;   % Minimum Support Threshold
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>> MCT=0.2;    % Minimum Confidence Threshold
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>> [FinalRules, Rules]=Apriori(T,MST,MCT);    % line
> >> containing
> >> > > >>> the
> >> > > >>>>>>>>>>>>>> error
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>> On 3/5/17, Eibe Frank <[hidden email]> wrote:
> >> > > >>>>>>>>>>>>>>> You can create a new attribute by combining nominal
> >> > > >>> attributes
> >> > > >>>>>>>>>>>>>>> using
> >> > > >>>>>>>>>>>>>>> the
> >> > > >>>>>>>>>>>>>>> CartesianProduct filter.
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> Regarding the reliability of the rules, take a look
> >> at the
> >> > > >>>>>>>>>>>>>>> literature
> >> > > >>>>>>>>>>>>>>> for
> >> > > >>>>>>>>>>>>>>> "predictive apriori" on Google Scholar. I don't know
> >> if
> >> > > >>>>>>>>>>>>>>> there
> >> > > >>>>>>>>>>>>>>> have been
> >> > > >>>>>>>>>>>>>>> any
> >> > > >>>>>>>>>>>>>>> extensive studies.
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> To get a rough idea of how well PredictiveApriori
> >> works for
> >> > > >>>>>>>>>>>>>>> your data,
> >> > > >>>>>>>>>>>>>>> regardless of the accuracy of individual rules
> >> considered
> >> > > >>>>>>>>>>>>>>> in
> >> > > >>>>>>>>>>>>>>> isolation,
> >> > > >>>>>>>>>>>>>>> you
> >> > > >>>>>>>>>>>>>>> could apply it to mine class association rules with
> >> the
> >> > > >>>>>>>>>>>>>>> JCBA
> >> > > >>>>>>>>>>>>>>> classifier
> >> > > >>>>>>>>>>>>>>> (from the classAssociationRules package) and use
> >> > > >>>>>>>>>>>>>>> cross-validation for
> >> > > >>>>>>>>>>>>>>> evaluation, similar to what we did in our paper.
> >> Obviously,
> >> > > >>> you
> >> > > >>>>>>>>>>>>>>> will
> >> > > >>>>>>>>>>>>>>> have
> >> > > >>>>>>>>>>>>>>> to
> >> > > >>>>>>>>>>>>>>> create an appropriate class attribute for each
> >> > > >>>>>>>>>>>>>>> attribute/attribute
> >> > > >>>>>>>>>>>>>>> combination that you are interested in (possibly using
> >> > > >>>>>>>>>>>>>>> CartesianProduct).
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> Here is an example command-line, running JCBA with
> >> > > >>>>>>>>>>>>>>> PredictiveApriori on
> >> > > >>>>>>>>>>>>>>> the
> >> > > >>>>>>>>>>>>>>> vote data (using the default class attribute). I got
> >> it to
> >> > > >>> only
> >> > > >>>>>>>>>>>>>>> output
> >> > > >>>>>>>>>>>>>>> the
> >> > > >>>>>>>>>>>>>>> top two rules for simplicity:
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> ===================
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> java weka.Run .JCBA -A ".PredictiveApriori -N 2" -t
> >> > > >>>>>>>>>>>>>>> ~/datasets/UCI/vote.arff
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> Options: -A ".PredictiveApriori -N 2"
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> Classification Rules (ordered):
> >> > > >>>>>>>>>>>>>>> ==========================
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> 1.       physician-fee-freeze=n 3 0
> >> > > >>>>>>>>>>>>>>> adoption-of-the-budget-resolution=y 2 1
> >> > > >>>>>>>>>>>>>>> ==>
> >> > > >>>>>>>>>>>>>>> Class=democrat     acc:(0.99),  (219),
> >> > > >>>>>>>>>>>>>>> 2.       crime=n 13 0 el-salvador-aid=n 4 0
> >> > > >>>>>>>>>>>>>>> adoption-of-the-budget-resolution=y
> >> > > >>>>>>>>>>>>>>> 2
> >> > > >>>>>>>>>>>>>>> 1  ==> Class=democrat     acc:(0.99),  (144),
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> Default Class: Class=republican
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> Additional Information:
> >> > > >>>>>>>>>>>>>>> Number of Classification Associations Rules generated
> >> by
> >> > > >>>>>>>>>>>>>>> Rule
> >> > > >>>>>>>>>>>>>>> Miner: 2
> >> > > >>>>>>>>>>>>>>> Number of Classification Rules: 2
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> Mining Time in sec.: 7.867
> >> > > >>>>>>>>>>>>>>> Pruning Time in sec. : 0.033
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> Time taken to build model: 7.91 seconds
> >> > > >>>>>>>>>>>>>>> Time taken to test model on training data: 0.02
> >> seconds
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> === Error on training data ===
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> Correctly Classified Instances         389
> >> > > >>>>>>>>>>>>>>> 89.4253 %
> >> > > >>>>>>>>>>>>>>> Incorrectly Classified Instances        46
> >> > > >>>>>>>>>>>>>>> 10.5747 %
> >> > > >>>>>>>>>>>>>>> Kappa statistic                          0.7877
> >> > > >>>>>>>>>>>>>>> Mean absolute error                      0.1057
> >> > > >>>>>>>>>>>>>>> Root mean squared error                  0.3252
> >> > > >>>>>>>>>>>>>>> Relative absolute error                 22.2991 %
> >> > > >>>>>>>>>>>>>>> Root relative squared error             66.7902 %
> >> > > >>>>>>>>>>>>>>> Total Number of Instances              435
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> === Detailed Accuracy By Class ===
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>             TP Rate  FP Rate  Precision  Recall
> >> > > >>>>>>>>>>>>>>> F-Measure
> >> > > >>>>>>>>>>>>>>> MCC
> >> > > >>>>>>>>>>>>>>> ROC Area  PRC Area  Class
> >> > > >>>>>>>>>>>>>>>             0.828    0.000    1.000      0.828
> >> 0.906
> >> > > >>>>>>>>>>>>>>> 0.806
> >> > > >>>>>>>>>>>>>>> 0.914     0.933     democrat
> >> > > >>>>>>>>>>>>>>>             1.000    0.172    0.785      1.000
> >> 0.880
> >> > > >>>>>>>>>>>>>>> 0.806
> >> > > >>>>>>>>>>>>>>> 0.914     0.785     republican
> >> > > >>>>>>>>>>>>>>> Weighted Avg.    0.894    0.067    0.917      0.894
> >> > > >>>>>>>>>>>>>>> 0.896
> >> > > >>>>>>>>>>>>>>> 0.806
> >> > > >>>>>>>>>>>>>>> 0.914     0.876
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> === Confusion Matrix ===
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> a   b   <-- classified as
> >> > > >>>>>>>>>>>>>>> 221  46 |   a = democrat
> >> > > >>>>>>>>>>>>>>> 0 168 |   b = republican
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> === Stratified cross-validation ===
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> Correctly Classified Instances         391
> >> > > >>>>>>>>>>>>>>> 89.8851 %
> >> > > >>>>>>>>>>>>>>> Incorrectly Classified Instances        44
> >> > > >>>>>>>>>>>>>>> 10.1149 %
> >> > > >>>>>>>>>>>>>>> Kappa statistic                          0.7957
> >> > > >>>>>>>>>>>>>>> Mean absolute error                      0.1011
> >> > > >>>>>>>>>>>>>>> Root mean squared error                  0.318
> >> > > >>>>>>>>>>>>>>> Relative absolute error                 21.3284 %
> >> > > >>>>>>>>>>>>>>> Root relative squared error             65.3201 %
> >> > > >>>>>>>>>>>>>>> Total Number of Instances              435
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> === Detailed Accuracy By Class ===
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>             TP Rate  FP Rate  Precision  Recall
> >> > > >>>>>>>>>>>>>>> F-Measure
> >> > > >>>>>>>>>>>>>>> MCC
> >> > > >>>>>>>>>>>>>>> ROC Area  PRC Area  Class
> >> > > >>>>>>>>>>>>>>>             0.843    0.012    0.991      0.843
> >> 0.911
> >> > > >>>>>>>>>>>>>>> 0.810
> >> > > >>>>>>>>>>>>>>> 0.915     0.932     democrat
> >> > > >>>>>>>>>>>>>>>             0.988    0.157    0.798      0.988
> >> 0.883
> >> > > >>>>>>>>>>>>>>> 0.810
> >> > > >>>>>>>>>>>>>>> 0.915     0.793     republican
> >> > > >>>>>>>>>>>>>>> Weighted Avg.    0.899    0.068    0.917      0.899
> >> > > >>>>>>>>>>>>>>> 0.900
> >> > > >>>>>>>>>>>>>>> 0.810
> >> > > >>>>>>>>>>>>>>> 0.915     0.878
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> === Confusion Matrix ===
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> a   b   <-- classified as
> >> > > >>>>>>>>>>>>>>> 225  42 |   a = democrat
> >> > > >>>>>>>>>>>>>>> 2 166 |   b = republican
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> ===================
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> The observed precision of classifications for class
> >> > > >>>>>>>>>>>>>>> democrat
> >> > > >>>>>>>>>>>>>>> estimated
> >> > > >>>>>>>>>>>>>>> by
> >> > > >>>>>>>>>>>>>>> cross-validation (under "Detailed Accuracy By Class")
> >> is
> >> > > >>> quite
> >> > > >>>>>>>>>>>>>>> close to
> >> > > >>>>>>>>>>>>>>> the
> >> > > >>>>>>>>>>>>>>> accuracy estimates listed for the individual rules in
> >> the
> >> > > >>>>>>>>>>>>>>> initial
> >> > > >>>>>>>>>>>>>>> output
> >> > > >>>>>>>>>>>>>>> of
> >> > > >>>>>>>>>>>>>>> the class association rules (0.99), so we can be
> >> reasonably
> >> > > >>>>>>>>>>>>>>> confident
> >> > > >>>>>>>>>>>>>>> in
> >> > > >>>>>>>>>>>>>>> this case that the rules are very accurate.
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> This process doesn't give you independent accuracy
> >> > > >>>>>>>>>>>>>>> estimates
> >> > > >>>>>>>>>>>>>>> for
> >> > > >>>>>>>>>>>>>>> individual
> >> > > >>>>>>>>>>>>>>> rules though. Assuming you have a reasonably large
> >> test
> >> > > >>>>>>>>>>>>>>> set,
> >> > > >>>>>>>>>>>>>>> you could
> >> > > >>>>>>>>>>>>>>> code
> >> > > >>>>>>>>>>>>>>> up individual rules in PMML and use the PMML
> >> classifier in
> >> > > >>> WEKA
> >> > > >>>>>>>>>>>>>>> for
> >> > > >>>>>>>>>>>>>>> each
> >> > > >>>>>>>>>>>>>>> rule to evaluate it on the test set
> >> > > >>>>>>>>>>>>>>> (http://wiki.pentaho.com/display/DATAMINING/PMML+
> >> > > >>> Support+in+Weka).
> >> > > >>>>>>>>>>>>>>> However,
> >> > > >>>>>>>>>>>>>>> you mentioned that you have a small dataset so this is
> >> > > >>> probably
> >> > > >>>>>>>>>>>>>>> not an
> >> > > >>>>>>>>>>>>>>> option for you.
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> Cheers,
> >> > > >>>>>>>>>>>>>>> Eibe
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>> On 5 Mar 2017, at 03:39, Bhupesh Rawat <
> >> [hidden email]>
> >> > > >>>>>>>>>>>>>>>> wrote:
> >> > > >>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>> Sir,
> >> > > >>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>> How to choose combination of attribute as a class
> >> > > >>>>>>>>>>>>>>>> attribute
> >> > > >>>>>>>>>>>>>>>> with Jrip
> >> > > >>>>>>>>>>>>>>>> or PART in weka 3.8.
> >> > > >>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>> Moreover i tried Predictive apriori on the dataset
> >> and as
> >> > > >>>>>>>>>>>>>>>> a
> >> > > >>>>>>>>>>>>>>>> result i
> >> > > >>>>>>>>>>>>>>>> found some rules with their respective accuracy. How
> >> > > >>> reliable
> >> > > >>>>>>>>>>>>>>>> are
> >> > > >>>>>>>>>>>>>>>> those rules based on this accuracy.
> >> > > >>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>> On Thu, Mar 2, 2017 at 2:10 AM, Eibe Frank
> >> > > >>>>>>>>>>>>>>>> <[hidden email]> wrote:
> >> > > >>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>> In WEKA 3.8/3.9, under
> >> > > >>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>> filters.unsupervised.attribute.CartesianProduct
> >> > > >>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>> Cheers,
> >> > > >>>>>>>>>>>>>>>>> Eibe
> >> > > >>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>> On 1/03/2017, at 6:13 PM, Bhupesh Rawat
> >> > > >>>>>>>>>>>>>>>>>> <[hidden email]
> >> > > >>>>
> >> > > >>>>>>>>>>>>>>>>>> wrote:
> >> > > >>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>> Thank you Sir, the problem has been fixed.
> >> > > >>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>> Moreover i would also like to use the combination
> >> of
> >> > > >>>>>>>>>>>>>>>>>> attributes for
> >> > > >>>>>>>>>>>>>>>>>> which  you suggested  the CartesionProduct filter.
> >> Where
> >> > > >>>>>>>>>>>>>>>>>> could i
> >> > > >>>>>>>>>>>>>>>>>> find
> >> > > >>>>>>>>>>>>>>>>>> this option?
> >> > > >>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>> On 2/28/17, Eibe Frank <[hidden email]> wrote:
> >> > > >>>>>>>>>>>>>>>>>>> What does the log say (see the ?log? tab next to
> >> the
> >> > > >>>>>>>>>>>>>>>>>>> ?status? tab)?
> >> > > >>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>> Cheers,
> >> > > >>>>>>>>>>>>>>>>>>> Eibe
> >> > > >>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>> On 27/02/2017, at 11:56 PM, Bhupesh Rawat
> >> > > >>>>>>>>>>>>>>>>>>>> <[hidden email]>
> >> > > >>>>>>>>>>>>>>>>>>>> wrote:
> >> > > >>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>> Sir,
> >> > > >>>>>>>>>>>>>>>>>>>> When I use the KnowledgeFlow GUI the status
> >> shown by
> >> > > >>>>>>>>>>>>>>>>>>>> two
> >> > > >>>>>>>>>>>>>>>>>>>> of the
> >> > > >>>>>>>>>>>>>>>>> components
> >> > > >>>>>>>>>>>>>>>>>>>> is interrupted(namely crossvalidationfoldmaker
> >> and
> >> > > >>>>>>>>>>>>>>>>>>>> J48)
> >> > > >>> as
> >> > > >>>>>>>>>>>>>>>>>>>> shown
> >> > > >>>>>>>>>>>>>>>>>>>> in
> >> > > >>>>>>>>>>>>>>>>>>>> the
> >> > > >>>>>>>>>>>>>>>>>>>> attached file. How to fix it?
> >> > > >>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>> On Mon, Feb 27, 2017 at 3:08 AM, Eibe Frank
> >> > > >>>>>>>>>>>>>>>>>>>> <[hidden email]>
> >> > > >>>>>>>>>>>>>>>>> wrote:
> >> > > >>>>>>>>>>>>>>>>>>>> In the Explorer, there is no way to turn off
> >> > > >>>>>>>>>>>>>>>>>>>> evaluation
> >> > > >>>>>>>>>>>>>>>>>>>> completely.
> >> > > >>>>>>>>>>>>>>>>>>>> You
> >> > > >>>>>>>>>>>>>>>>>>>> could use the command-line interface or the
> >> > > >>> KnowledgeFlow
> >> > > >>>>>>>>>>>>>>>>>>>> GUI
> >> > > >>>>>>>>>>>>>>>>>>>> though.
> >> > > >>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>> Having said this, if you evaluate on the
> >> training set,
> >> > > >>> the
> >> > > >>>>>>>>>>>>>>>>>>>> runtime
> >> > > >>>>>>>>>>>>>>>>>>>> overhead is quite small if you apply a rule
> >> learner.
> >> > > >>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>> Note also that the Explorer always outputs the
> >> > > >>>>>>>>>>>>>>>>>>>> classification
> >> > > >>>>>>>>>>>>>>>>>>>> model
> >> > > >>>>>>>>>>>>>>>>>>>> for
> >> > > >>>>>>>>>>>>>>>>>>>> the *full* dataset loaded into the Preprocess
> >> panel,
> >> > > >>>>>>>>>>>>>>>>>>>> regardless of
> >> > > >>>>>>>>>>>>>>>>> which
> >> > > >>>>>>>>>>>>>>>>>>>> evaluation metric you choose, i.e., you will get
> >> the
> >> > > >>> rule
> >> > > >>>>>>>>>>>>>>>>>>>> set for
> >> > > >>>>>>>>>>>>>>>>>>>> the
> >> > > >>>>>>>>>>>>>>>>> full
> >> > > >>>>>>>>>>>>>>>>>>>> dataset regardless of the evaluation method you
> >> use.
> >> > > >>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>> Cheers,
> >> > > >>>>>>>>>>>>>>>>>>>> Eibe
> >> > > >>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>> On 26/02/2017, at 8:07 PM, Bhupesh Rawat
> >> > > >>>>>>>>>>>>>>>>>>>>> <[hidden email]>
> >> > > >>>>>>>>>>>>>>>>>>>>> wrote:
> >> > > >>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>> Sir,
> >> > > >>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>> How could i perform these two task
> >> > > >>>>>>>>>>>>>>>>>>>>> seperately(applying
> >> > > >>>>>>>>>>>>>>>>>>>>> classification
> >> > > >>>>>>>>>>>>>>>>>>>>> rule learner and estimating classification
> >> accuracy).
> >> > > >>> The
> >> > > >>>>>>>>>>>>>>>>>>>>> accuracy
> >> > > >>>>>>>>>>>>>>>>>>>>> is
> >> > > >>>>>>>>>>>>>>>>>>>>> estimated each time i run the classifier on the
> >> > > >>> dataset.
> >> > > >>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>> On 2/24/17, Eibe Frank <[hidden email]>
> >> wrote:
> >> > > >>>>>>>>>>>>>>>>>>>>>> No, not really. However, the dataset is quite
> >> small.
> >> > > >>> You
> >> > > >>>>>>>>>>>>>>>>>>>>>> could
> >> > > >>>>>>>>>>>>>>>>>>>>>> just
> >> > > >>>>>>>>>>>>>>>>> run
> >> > > >>>>>>>>>>>>>>>>>>>>>> a
> >> > > >>>>>>>>>>>>>>>>>>>>>> classification rule learner such as JRip or
> >> PART on
> >> > > >>> the
> >> > > >>>>>>>>>>>>>>>>>>>>>> data,
> >> > > >>>>>>>>>>>>>>>>> treating
> >> > > >>>>>>>>>>>>>>>>>>>>>> each
> >> > > >>>>>>>>>>>>>>>>>>>>>> of the attributes in turn as the class
> >> attribute.
> >> > > >>>>>>>>>>>>>>>>>>>>>> Then
> >> > > >>>>>>>>>>>>>>>>>>>>>> you can
> >> > > >>>>>>>>>>>>>>>>> estimate
> >> > > >>>>>>>>>>>>>>>>>>>>>> classification accuracy using cross-validation.
> >> > > >>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>> You could also create combinations of
> >> attributes
> >> > > >>>>>>>>>>>>>>>>>>>>>> using
> >> > > >>>>>>>>>>>>>>>>>>>>>> the
> >> > > >>>>>>>>>>>>>>>>>>>>>> CartesionProduct
> >> > > >>>>>>>>>>>>>>>>>>>>>> filter.
> >> > > >>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>> Cheers,
> >> > > >>>>>>>>>>>>>>>>>>>>>> Eibe
> >> > > >>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>> On 24/02/2017, at 3:11 AM, Bhupesh Rawat
> >> > > >>>>>>>>>>>>>>>>>>>>>>> <[hidden email]>
> >> > > >>>>>>>>>>>>>>>>>>>>>>> wrote:
> >> > > >>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>> I have a small dataset which contains student
> >> > > >>> enrolment
> >> > > >>>>>>>>>>>>>>>>>>>>>>> data in
> >> > > >>>>>>>>>>>>>>>>>>>>>>> various courses. If a student has selected a
> >> > > >>> particular
> >> > > >>>>>>>>>>>>>>>>>>>>>>> course
> >> > > >>>>>>>>>>>>>>>>>>>>>>> it
> >> > > >>>>>>>>>>>>>>>>>>>>>>> is
> >> > > >>>>>>>>>>>>>>>>>>>>>>> indicated by ?Y? else ?N? is used. I have also
> >> > > >>> attached
> >> > > >>>>>>>>>>>>>>>>>>>>>>> a file
> >> > > >>>>>>>>>>>>>>>>>>>>>>> for
> >> > > >>>>>>>>>>>>>>>>>>>>>>> better understanding of the dataset. I am
> >> > > >>>>>>>>>>>>>>>>>>>>>>> interested
> >> > > >>> in
> >> > > >>>>>>>>>>>>>>>>>>>>>>> knowing
> >> > > >>>>>>>>>>>>>>>>>>>>>>> if
> >> > > >>>>>>>>>>>>>>>>> it
> >> > > >>>>>>>>>>>>>>>>>>>>>>> is possible to measure the accuracy of the
> >> > > >>> association
> >> > > >>>>>>>>>>>>>>>>>>>>>>> rules
> >> > > >>>>>>>>>>>>>>>>>>>>>>> with
> >> > > >>>>>>>>>>>>>>>>> this
> >> > > >>>>>>>>>>>>>>>>>>>>>>> dataset by the proposed approach in your
> >> paper.
> >> > > >>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>> On 2/23/17, Bhupesh Rawat <[hidden email]>
> >> wrote:
> >> > > >>>>>>>>>>>>>>>>>>>>>>>> Thank you so much for the response!!
> >> > > >>>>>>>>>>>>>>>>>>>>>>>> On Feb 23, 2017 8:26 AM, "Eibe Frank"
> >> > > >>>>>>>>>>>>>>>>>>>>>>>> <[hidden email]>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>> wrote:
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> You mean beyond confidence, lift, or one the
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> other
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> metrics
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> that
> >> > > >>>>>>>>>>>>>>>>> you
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> can
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> get in the output of each rule? This is a
> >> tough
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> question. One
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> way
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> may be
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> to
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> use the association rule mining algorithm to
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> build
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> classification
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> rules
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> and
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> then evaluate the accuracy of those
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> classification
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> rules. We
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> had
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> a
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> paper
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> on
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> this quite a while back:
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> Mutter, S., Hall, M., & Frank, E. (2004,
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> December).
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> Using
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> classification
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> to evaluate the output of confidence-based
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> association rule
> >> > > >>>>>>>>>>>>>>>>> mining.
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> In
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> Australasian Joint Conference on Artificial
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> Intelligence (pp.
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> 538-549).
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> Springer Berlin Heidelberg.
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> I suppose you could also evaluate the
> >> individual
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> association
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> rules
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> on a
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> separate test set, by computing the
> >> confidence
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> measure, etc.,
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> on
> >> > > >>>>>>>>>>>>>>>>> the
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> test
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> set for each rule, but this functionality
> >> is not
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> provided by
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> WEKA.
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> Cheers,
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> Eibe
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> On 23/02/2017, at 12:46 AM, Bhupesh Rawat
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> <[hidden email]>
> >> > > >>>>>>>>>>>>>>>>> wrote:
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Dear Sir/Madam
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> I have discovered some rules through weka.
> >> Could
> >> > > >>> you
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> tell me
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> how
> >> > > >>>>>>>>>>>>>>>>> to
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> measure  the accuracy of those rules.
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> On Tue, Feb 14, 2017 at 3:44 AM, Peter
> >> Reutemann
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> <[hidden email]>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> wrote:
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> The size of the final Apriori model as a
> >> > > >>> serialised
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> Java
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> object
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> can
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> be
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> established saving it to a file and
> >> considering
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> the
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> file
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> size.
> >> > > >>>>>>>>>>>>>>>>> Note
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> that
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> this is different from the size of the
> >> object in
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> memory (see,
> >> > > >>>>>>>>>>>>>>>>> e.g.,
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> http://stackoverflow.com/
> >> > > >>> questions/7146559/serialized-
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> object-size-vs-in-memory-
> >> > > >>> object-size-in-java#7146941).
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> I don?t know of a good way to measure peak
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> memory
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>> consumption
> >> > > >>>>>>>>>>>>>>>>> of a
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> Java program (after garbage collection). A
> >> crude
> >> > > >>> way
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> would be
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> to
> >> > > >>>>>>>>>>>>>>>>> run
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> the
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> program from the command-line (to avoid
> >> overhead
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> associated
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> with
> >> > > >>>>>>>>>>>>>>>>> the
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> GUIs)
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> with different maximum heap sizes, e.g.,
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> increasing
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> the heap
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> size
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> until
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> the
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> program runs through. Another option is to
> >> look
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> at
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> the heap
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> size
> >> > > >>>>>>>>>>>>>>>>> in
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> a
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> profiler (e.g., visualvm), enforcing garbage
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> collection
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> before
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> a
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> readout.
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> You can use the sizeofag javaagent for
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> determining
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> the size
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> of
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> a
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Java
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> object:
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> https://github.com/fracpete/sizeofag
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Credits to Maxim Zakharenkov, who wrote the
> >> > > >>> original
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> code.
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Cheers, Peter
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> --
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Peter Reutemann
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Dept. of Computer Science
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> University of Waikato, NZ
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> +64 (7) 858-5174
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> http://www.cms.waikato.ac.nz/~fracpete/
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> http://www.data-mining.co.nz/
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> ______________________________
> >> _________________
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> mailman/listinfo/wekalist
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> List etiquette:
> >> http://www.cs.waikato.ac.nz/~
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> ml/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> --
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Thanks & Regards
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Bhupesh Rawat.
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Ph.D Scholar
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Department of Computer Science,Babasaheb
> >> Bhimrao
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Ambedkar
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> University
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Ph. No: +91-9897065948
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> ..............................
> >> > > >>> ..............................
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> ..............................
> >> > > >>> .................................
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> *A man is the best judge of himself and he
> >> has
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> to
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> pay the
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> price
> >> > > >>>>>>>>>>>>>>>>> for
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> what
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> he
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> does.*
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> ..............................
> >> > > >>> ..............................
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> ..............................
> >> > > >>> .................................
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> ______________________________
> >> _________________
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> mailman/listinfo/wekalist
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>> List etiquette:
> >> http://www.cs.waikato.ac.nz/~
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> ml/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> ______________________________
> >> _________________
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> mailman/listinfo/wekalist
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> List etiquette:
> >> http://www.cs.waikato.ac.nz/~
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>> ml/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>> --
> >> > > >>>>>>>>>>>>>>>>>>>>>>> Thanks & Regards
> >> > > >>>>>>>>>>>>>>>>>>>>>>> Bhupesh Rawat.
> >> > > >>>>>>>>>>>>>>>>>>>>>>> Ph.D Scholar
> >> > > >>>>>>>>>>>>>>>>>>>>>>> Department of Computer Science,Babasaheb
> >> Bhimrao
> >> > > >>>>>>>>>>>>>>>>>>>>>>> Ambedkar
> >> > > >>>>>>>>>>>>>>>>>>>>>>> University
> >> > > >>>>>>>>>>>>>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > >>>>>>>>>>>>>>>>>>>>>>> Ph. No: +91-9897065948
> >> > > >>>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>>> ..............................
> >> > > >>> ..............................
> >> > > >>>>>>>>>>>>>>>>> ..............................
> >> > > >>> .................................
> >> > > >>>>>>>>>>>>>>>>>>>>>>> *A man is the best judge of himself and he
> >> has to
> >> > > >>>>>>>>>>>>>>>>>>>>>>> pay
> >> > > >>>>>>>>>>>>>>>>>>>>>>> the price
> >> > > >>>>>>>>>>>>>>>>>>>>>>> for
> >> > > >>>>>>>>>>>>>>>>>>>>>>> what
> >> > > >>>>>>>>>>>>>>>>>>>>>>> he
> >> > > >>>>>>>>>>>>>>>>>>>>>>> does.*
> >> > > >>>>>>>>>>>>>>>>>>>>>>> ..............................
> >> > > >>> ..............................
> >> > > >>>>>>>>>>>>>>>>> ..............................
> >> > > >>> .................................
> >> > > >>>>>>>>>>>>>>>>>>>>>>> <students' data after
> >> > > >>>>>>>>>>>>>>>>>>>>>>> preprocessin.xlsx>____________
> >> > > >>> ___________________________________
> >> > > >>>>>>>>>>>>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/mai
> >> lman/listinfo/wekalist
> >> > > >>>>>>>>>>>>>>>>>>>>>>> List etiquette:
> >> > > >>>>>>>>>>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~
> >> > > >>> ml/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>> ______________________________
> >> _________________
> >> > > >>>>>>>>>>>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/mai
> >> lman/listinfo/wekalist
> >> > > >>>>>>>>>>>>>>>>>>>>>> List etiquette:
> >> > > >>>>>>>>>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~
> >> > > >>> ml/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>> --
> >> > > >>>>>>>>>>>>>>>>>>>>> Thanks & Regards
> >> > > >>>>>>>>>>>>>>>>>>>>> Bhupesh Rawat.
> >> > > >>>>>>>>>>>>>>>>>>>>> Ph.D Scholar
> >> > > >>>>>>>>>>>>>>>>>>>>> Department of Computer Science,Babasaheb Bhimrao
> >> > > >>> Ambedkar
> >> > > >>>>>>>>>>>>>>>>>>>>> University
> >> > > >>>>>>>>>>>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > >>>>>>>>>>>>>>>>>>>>> Ph. No: +91-9897065948
> >> > > >>>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>> ..............................
> >> > > >>> ..............................
> >> > > >>>>>>>>>>>>>>>>> ..............................
> >> > > >>> .................................
> >> > > >>>>>>>>>>>>>>>>>>>>> *A man is the best judge of himself and he has
> >> to pay
> >> > > >>> the
> >> > > >>>>>>>>>>>>>>>>>>>>> price
> >> > > >>>>>>>>>>>>>>>>>>>>> for
> >> > > >>>>>>>>>>>>>>>>> what
> >> > > >>>>>>>>>>>>>>>>>>>>> he
> >> > > >>>>>>>>>>>>>>>>>>>>> does.*
> >> > > >>>>>>>>>>>>>>>>>>>>> ..............................
> >> > > >>> ..............................
> >> > > >>>>>>>>>>>>>>>>> ..............................
> >> > > >>> .................................
> >> > > >>>>>>>>>>>>>>>>>>>>> _______________________________________________
> >> > > >>>>>>>>>>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/mai
> >> lman/listinfo/wekalist
> >> > > >>>>>>>>>>>>>>>>>>>>> List etiquette:
> >> > > >>>>>>>>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~
> >> > > >>> ml/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>> _______________________________________________
> >> > > >>>>>>>>>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/mai
> >> lman/listinfo/wekalist
> >> > > >>>>>>>>>>>>>>>>>>>> List etiquette:
> >> > > >>>>>>>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~
> >> > > >>> ml/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>> --
> >> > > >>>>>>>>>>>>>>>>>>>> Thanks & Regards
> >> > > >>>>>>>>>>>>>>>>>>>> Bhupesh Rawat.
> >> > > >>>>>>>>>>>>>>>>>>>> Ph.D Scholar
> >> > > >>>>>>>>>>>>>>>>>>>> Department of Computer Science,Babasaheb Bhimrao
> >> > > >>> Ambedkar
> >> > > >>>>>>>>>>>>>>>>>>>> University
> >> > > >>>>>>>>>>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > >>>>>>>>>>>>>>>>>>>> Ph. No: +91-9897065948
> >> > > >>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>> ..............................
> >> > > >>> ..............................
> >> > > >>>>>>>>>>>>>>>>> ..............................
> >> > > >>> .................................
> >> > > >>>>>>>>>>>>>>>>>>>> *A man is the best judge of himself and he has
> >> to pay
> >> > > >>> the
> >> > > >>>>>>>>>>>>>>>>>>>> price
> >> > > >>>>>>>>>>>>>>>>>>>> for
> >> > > >>>>>>>>>>>>>>>>> what
> >> > > >>>>>>>>>>>>>>>>>>>> he
> >> > > >>>>>>>>>>>>>>>>>>>> does.*
> >> > > >>>>>>>>>>>>>>>>>>>> ..............................
> >> > > >>> ..............................
> >> > > >>>>>>>>>>>>>>>>> ..............................
> >> > > >>> .................................
> >> > > >>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>>> <knowledge flow
> >> > > >>>>>>>>>>>>>>>>>>>> interuppted.docx>_____________
> >> > > >>> __________________________________
> >> > > >>>>>>>>>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/mai
> >> lman/listinfo/wekalist
> >> > > >>>>>>>>>>>>>>>>>>>> List etiquette:
> >> > > >>>>>>>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~
> >> > > >>> ml/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>> _______________________________________________
> >> > > >>>>>>>>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/mai
> >> lman/listinfo/wekalist
> >> > > >>>>>>>>>>>>>>>>>>> List etiquette:
> >> > > >>>>>>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~
> >> > > >>> ml/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>> --
> >> > > >>>>>>>>>>>>>>>>>> Thanks & Regards
> >> > > >>>>>>>>>>>>>>>>>> Bhupesh Rawat.
> >> > > >>>>>>>>>>>>>>>>>> Ph.D Scholar
> >> > > >>>>>>>>>>>>>>>>>> Department of Computer Science,Babasaheb Bhimrao
> >> > > >>>>>>>>>>>>>>>>>> Ambedkar
> >> > > >>>>>>>>>>>>>>>>>> University
> >> > > >>>>>>>>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > >>>>>>>>>>>>>>>>>> Ph. No: +91-9897065948
> >> > > >>>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>>> ..............................
> >> > > >>> ..............................
> >> > > >>>>>>>>>>>>>>>>> ..............................
> >> > > >>> .................................
> >> > > >>>>>>>>>>>>>>>>>> *A man is the best judge of himself and he has to
> >> pay
> >> > > >>>>>>>>>>>>>>>>>> the
> >> > > >>>>>>>>>>>>>>>>>> price for
> >> > > >>>>>>>>>>>>>>>>>> what
> >> > > >>>>>>>>>>>>>>>>> he
> >> > > >>>>>>>>>>>>>>>>>> does.*
> >> > > >>>>>>>>>>>>>>>>>> ..............................
> >> > > >>> ..............................
> >> > > >>>>>>>>>>>>>>>>> ..............................
> >> > > >>> .................................
> >> > > >>>>>>>>>>>>>>>>>> _______________________________________________
> >> > > >>>>>>>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/
> >> > > >>>>>>>>>>>>>>>>> mailman/listinfo/wekalist
> >> > > >>>>>>>>>>>>>>>>>> List etiquette: http://www.cs.waikato.ac.nz/~
> >> > > >>>>>>>>>>>>>>>>> ml/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>> _______________________________________________
> >> > > >>>>>>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>>>>>> https://list.waikato.ac.nz/
> >> > > >>>>>>>>>>>>>>>>> mailman/listinfo/wekalist
> >> > > >>>>>>>>>>>>>>>>> List etiquette: http://www.cs.waikato.ac.nz/~
> >> > > >>>>>>>>>>>>>>>>> ml/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>> --
> >> > > >>>>>>>>>>>>>>>> Thanks & Regards
> >> > > >>>>>>>>>>>>>>>> Bhupesh Rawat.
> >> > > >>>>>>>>>>>>>>>> Ph.D Scholar
> >> > > >>>>>>>>>>>>>>>> Department of Computer Science,Babasaheb Bhimrao
> >> Ambedkar
> >> > > >>>>>>>>>>>>>>>> University
> >> > > >>>>>>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > >>>>>>>>>>>>>>>> Ph. No: +91-9897065948
> >> > > >>>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>>> ..............................
> >> > > >>> ............................................................
> >> > > >>> .................................
> >> > > >>>>>>>>>>>>>>>> *A man is the best judge of himself and he has to
> >> pay the
> >> > > >>>>>>>>>>>>>>>> price for
> >> > > >>>>>>>>>>>>>>>> what
> >> > > >>>>>>>>>>>>>>>> he
> >> > > >>>>>>>>>>>>>>>> does.*
> >> > > >>>>>>>>>>>>>>>> ..............................
> >> > > >>> ............................................................
> >> > > >>> .................................
> >> > > >>>>>>>>>>>>>>>> _______________________________________________
> >> > > >>>>>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
> >> > > >>>>>>>>>>>>>>>> List etiquette:
> >> > > >>>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.
> >> > > >>> html
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>> _______________________________________________
> >> > > >>>>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
> >> > > >>>>>>>>>>>>>>> List etiquette:
> >> > > >>>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.
> >> > > >>> html
> >> > > >>>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>> --
> >> > > >>>>>>>>>>>>>> Thanks & Regards
> >> > > >>>>>>>>>>>>>> Bhupesh Rawat.
> >> > > >>>>>>>>>>>>>> Ph.D Scholar
> >> > > >>>>>>>>>>>>>> Department of Computer Science,Babasaheb Bhimrao
> >> Ambedkar
> >> > > >>>>>>>>>>>>>> University
> >> > > >>>>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > >>>>>>>>>>>>>> Ph. No: +91-9897065948
> >> > > >>>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>>> ..............................
> >> ..............................
> >> > > >>> ...............................................................
> >> > > >>>>>>>>>>>>>> *A man is the best judge of himself and he has to pay
> >> the
> >> > > >>> price
> >> > > >>>>>>>>>>>>>> for what
> >> > > >>>>>>>>>>>>>> he
> >> > > >>>>>>>>>>>>>> does.*
> >> > > >>>>>>>>>>>>>> ..............................
> >> ..............................
> >> > > >>> ...............................................................
> >> > > >>>>>>>>>>>>>> <dataset_matlab.xls>__________
> >> ______________________________
> >> > > >>> _______
> >> > > >>>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
> >> > > >>>>>>>>>>>>>> List etiquette:
> >> > > >>>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.
> >> > > >>> html
> >> > > >>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>> _______________________________________________
> >> > > >>>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
> >> > > >>>>>>>>>>>>> List etiquette:
> >> > > >>>>>>>>>>>>> http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.
> >> > > >>> html
> >> > > >>>>>>>>>>>>>
> >> > > >>>>>>>>>>>>
> >> > > >>>>>>>>>>>>
> >> > > >>>>>>>>>>>> --
> >> > > >>>>>>>>>>>> Thanks & Regards
> >> > > >>>>>>>>>>>> Bhupesh Rawat.
> >> > > >>>>>>>>>>>> Ph.D Scholar
> >> > > >>>>>>>>>>>> Department of Computer Science,Babasaheb Bhimrao Ambedkar
> >> > > >>>>>>>>>>>> University
> >> > > >>>>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > >>>>>>>>>>>> Ph. No: +91-9897065948
> >> > > >>>>>>>>>>>>
> >> > > >>>>>>>>>>>> ..............................
> >> ..............................
> >> > > >>> ...............................................................
> >> > > >>>>>>>>>>>> *A man is the best judge of himself and he has to pay the
> >> > > >>>>>>>>>>>> price
> >> > > >>>>>>>>>>>> for what
> >> > > >>>>>>>>>>>> he
> >> > > >>>>>>>>>>>> does.*
> >> > > >>>>>>>>>>>> ..............................
> >> ..............................
> >> > > >>> ...............................................................
> >> > > >>>>>>>>>>>> _______________________________________________
> >> > > >>>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
> >> > > >>>>>>>>>>>> List etiquette:
> >> > > >>>>>>>>>>>> http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>>>
> >> > > >>>>>>>>>>> _______________________________________________
> >> > > >>>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
> >> > > >>>>>>>>>>> List etiquette:
> >> > > >>>>>>>>>>> http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>>>
> >> > > >>>>>>>>>>
> >> > > >>>>>>>>>>
> >> > > >>>>>>>>>> --
> >> > > >>>>>>>>>> Thanks & Regards
> >> > > >>>>>>>>>> Bhupesh Rawat.
> >> > > >>>>>>>>>> Ph.D Scholar
> >> > > >>>>>>>>>> Department of Computer Science,Babasaheb Bhimrao Ambedkar
> >> > > >>> University
> >> > > >>>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > >>>>>>>>>> Ph. No: +91-9897065948
> >> > > >>>>>>>>>>
> >> > > >>>>>>>>>> ..............................
> >> ..............................
> >> > > >>> ...............................................................
> >> > > >>>>>>>>>> *A man is the best judge of himself and he has to pay the
> >> price
> >> > > >>> for
> >> > > >>>>>>>>>> what he
> >> > > >>>>>>>>>> does.*
> >> > > >>>>>>>>>> ..............................
> >> ..............................
> >> > > >>> ...............................................................
> >> > > >>>>>>>>>> _______________________________________________
> >> > > >>>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
> >> > > >>>>>>>>>> List etiquette:
> >> > > >>>>>>>>>> http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>
> >> > > >>>>>>>>> _______________________________________________
> >> > > >>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
> >> > > >>>>>>>>> List etiquette:
> >> > > >>>>>>>>> http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>>
> >> > > >>>>>>>>>
> >> > > >>>>>>>>>
> >> > > >>>>>>>>> --
> >> > > >>>>>>>>> Thanks & Regards
> >> > > >>>>>>>>> Bhupesh Rawat.
> >> > > >>>>>>>>> Ph.D Scholar
> >> > > >>>>>>>>> Department of Computer Science,Babasaheb Bhimrao Ambedkar
> >> > > >>> University
> >> > > >>>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > >>>>>>>>> Ph. No: +91-9897065948
> >> > > >>>>>>>>>
> >> > > >>>>>>>>> ..............................
> >> ..............................
> >> > > >>> ...............................................................
> >> > > >>>>>>>>> *A man is the best judge of himself and he has to pay the
> >> price
> >> > > >>>>>>>>> for
> >> > > >>>>>>>>> what he
> >> > > >>>>>>>>> does.*
> >> > > >>>>>>>>> ..............................
> >> ..............................
> >> > > >>> ...............................................................
> >> > > >>>>>>>>>
> >> > > >>>>>>>>>
> >> > > >>>>>>>>>
> >> > > >>>>>>>>> _______________________________________________
> >> > > >>>>>>>>> Wekalist mailing list
> >> > > >>>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>>> List info and subscription status:
> >> > > >>>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
> >> > > >>>>>>>>> List etiquette:
> >> > > >>>>>>>>> http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.html
> >> > > >>>>>>>>
> >> > > >>>>>>>> _______________________________________________
> >> > > >>>>>>>> Wekalist mailing list
> >> > > >>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>> List info and subscription status:
> >> > > >>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
> >> > > >>>>>>>> List etiquette:
> >> > > >>>>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.h
> >> tml
> >> > > >>>>>>>>
> >> > > >>>>>>>>
> >> > > >>>>>>>>
> >> > > >>>>>>>> --
> >> > > >>>>>>>> Thanks & Regards
> >> > > >>>>>>>> Bhupesh Rawat.
> >> > > >>>>>>>> Ph.D Scholar
> >> > > >>>>>>>> Department of Computer Science,Babasaheb Bhimrao Ambedkar
> >> > > >>>>>>>> University
> >> > > >>>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > >>>>>>>> Ph. No: +91-9897065948
> >> > > >>>>>>>>
> >> > > >>>>>>>> ............................................................
> >> > > >>> ...............................................................
> >> > > >>>>>>>> *A man is the best judge of himself and he has to pay the
> >> price
> >> > > >>>>>>>> for
> >> > > >>> what
> >> > > >>>>>>>> he
> >> > > >>>>>>>> does.*
> >> > > >>>>>>>> ............................................................
> >> > > >>> ...............................................................
> >> > > >>>>>>>>
> >> > > >>>>>>>>
> >> > > >>>>>>>>
> >> > > >>>>>>>> _______________________________________________
> >> > > >>>>>>>> Wekalist mailing list
> >> > > >>>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>>> List info and subscription status:
> >> > > >>>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
> >> > > >>>>>>>> List etiquette:
> >> > > >>>>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.h
> >> tml
> >> > > >>>>>>>
> >> > > >>>>>>> _______________________________________________
> >> > > >>>>>>> Wekalist mailing list
> >> > > >>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>> List info and subscription status:
> >> > > >>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
> >> > > >>>>>>> List etiquette:
> >> > > >>>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.h
> >> tml
> >> > > >>>>>>>
> >> > > >>>>>>>
> >> > > >>>>>>>
> >> > > >>>>>>> --
> >> > > >>>>>>> Thanks & Regards
> >> > > >>>>>>> Bhupesh Rawat.
> >> > > >>>>>>> Ph.D Scholar
> >> > > >>>>>>> Department of Computer Science,Babasaheb Bhimrao Ambedkar
> >> > > >>>>>>> University
> >> > > >>>>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > >>>>>>> Ph. No: +91-9897065948
> >> > > >>>>>>>
> >> > > >>>>>>> ............................................................
> >> > > >>> ...............................................................
> >> > > >>>>>>> *A man is the best judge of himself and he has to pay the
> >> price for
> >> > > >>> what
> >> > > >>>>>>> he
> >> > > >>>>>>> does.*
> >> > > >>>>>>> ............................................................
> >> > > >>> ...............................................................
> >> > > >>>>>>>
> >> > > >>>>>>>
> >> > > >>>>>>>
> >> > > >>>>>>> _______________________________________________
> >> > > >>>>>>> Wekalist mailing list
> >> > > >>>>>>> Send posts to: [hidden email]
> >> > > >>>>>>> List info and subscription status:
> >> > > >>>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
> >> > > >>>>>>> List etiquette:
> >> > > >>>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.h
> >> tml
> >> > > >>>>>>
> >> > > >>>>>> _______________________________________________
> >> > > >>>>>> Wekalist mailing list
> >> > > >>>>>> Send posts to: [hidden email]
> >> > > >>>>>> List info and subscription status:
> >> > > >>>>>> https://list.waikato.ac.nz/mailman/listinfo/wekalist
> >> > > >>>>>> List etiquette:
> >> > > >>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.h
> >> tml
> >> > > >>>>>>
> >> > > >>>>>
> >> > > >>>>>
> >> > > >>>>> --
> >> > > >>>>> Thanks & Regards
> >> > > >>>>> Bhupesh Rawat.
> >> > > >>>>> Ph.D Scholar
> >> > > >>>>> Department of Computer Science,Babasaheb Bhimrao Ambedkar
> >> University
> >> > > >>>>> Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > >>>>> Ph. No: +91-9897065948
> >> > > >>>>>
> >> > > >>>>> ............................................................
> >> > > >>> ...............................................................
> >> > > >>>>> *A man is the best judge of himself and he has to pay the price
> >> for
> >> > > >>> what he
> >> > > >>>>> does.*
> >> > > >>>>> ............................................................
> >> > > >>> ...............................................................
> >> > > >>>>> _______________________________________________
> >> > > >>>>> Wekalist mailing list
> >> > > >>>>> Send posts to: [hidden email]
> >> > > >>>>> List info and subscription status: https://list.waikato.ac.nz/
> >> > > >>> mailman/listinfo/wekalist
> >> > > >>>>> List etiquette: http://www.cs.waikato.ac.nz/~
> >> > > >>> ml/weka/mailinglist_etiquette.html
> >> > > >>>>
> >> > > >>>> _______________________________________________
> >> > > >>>> Wekalist mailing list
> >> > > >>>> Send posts to: [hidden email]
> >> > > >>>> List info and subscription status: https://list.waikato.ac.nz/
> >> > > >>> mailman/listinfo/wekalist
> >> > > >>>> List etiquette: http://www.cs.waikato.ac.nz/~
> >> > > >>> ml/weka/mailinglist_etiquette.html
> >> > > >>>>
> >> > > >>>>
> >> > > >>>>
> >> > > >>>> --
> >> > > >>>> Thanks & Regards
> >> > > >>>> Bhupesh Rawat.
> >> > > >>>> Ph.D Scholar
> >> > > >>>> Department of Computer Science,Babasaheb Bhimrao Ambedkar
> >> University
> >> > > >>>> Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > >>>> Ph. No: +91-9897065948
> >> > > >>>>
> >> > > >>>> ............................................................
> >> > > >>> ...............................................................
> >> > > >>>> *A man is the best judge of himself and he has to pay the price
> >> for
> >> > > >>>> what
> >> > > >>> he
> >> > > >>>> does.*
> >> > > >>>> ............................................................
> >> > > >>> ...............................................................
> >> > > >>>>
> >> > > >>>>
> >> > > >>>>
> >> > > >>>> _______________________________________________
> >> > > >>>> Wekalist mailing list
> >> > > >>>> Send posts to: [hidden email]
> >> > > >>>> List info and subscription status: https://list.waikato.ac.nz/
> >> > > >>> mailman/listinfo/wekalist
> >> > > >>>> List etiquette: http://www.cs.waikato.ac.nz/~
> >> > > >>> ml/weka/mailinglist_etiquette.html
> >> > > >>>
> >> > > >>> _______________________________________________
> >> > > >>> Wekalist mailing list
> >> > > >>> Send posts to: [hidden email]
> >> > > >>> List info and subscription status: https://list.waikato.ac.nz/
> >> > > >>> mailman/listinfo/wekalist
> >> > > >>> List etiquette: http://www.cs.waikato.ac.nz/~
> >> > > >>> ml/weka/mailinglist_etiquette.html
> >> > > >>>
> >> > > >>
> >> > > >
> >> > > >
> >> > > > --
> >> > > > Thanks & Regards
> >> > > > Bhupesh Rawat.
> >> > > > Ph.D Scholar
> >> > > > Department of Computer Science,Babasaheb Bhimrao Ambedkar University
> >> > > > Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > > Ph. No: +91-9897065948
> >> > > >
> >> > > > ............................................................
> >> ...............................................................
> >> > > > *A man is the best judge of himself and he has to pay the price for
> >> what he
> >> > > > does.*
> >> > > > ............................................................
> >> ...............................................................
> >> > > > _______________________________________________
> >> > > > Wekalist mailing list
> >> > > > Send posts to: [hidden email]
> >> > > > List info and subscription status: https://list.waikato.ac.nz/mai
> >> lman/listinfo/wekalist
> >> > > > List etiquette: http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.html
> >> > >
> >> > > _______________________________________________
> >> > > Wekalist mailing list
> >> > > Send posts to: [hidden email]
> >> > > List info and subscription status: https://list.waikato.ac.nz/mai
> >> lman/listinfo/wekalist
> >> > > List etiquette: http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.html
> >> > >
> >> > >
> >> > >
> >> > > --
> >> > > Thanks & Regards
> >> > > Bhupesh Rawat.
> >> > > Ph.D Scholar
> >> > > Department of Computer Science,Babasaheb Bhimrao Ambedkar University
> >> > > Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > > Ph. No: +91-9897065948
> >> > >
> >> > > ............................................................
> >> ...............................................................
> >> > > *A man is the best judge of himself and he has to pay the price for
> >> what he
> >> > > does.*
> >> > > ............................................................
> >> ...............................................................
> >> > >
> >> > >
> >> > >
> >> > > _______________________________________________
> >> > > Wekalist mailing list
> >> > > Send posts to: [hidden email]
> >> > > List info and subscription status: https://list.waikato.ac.nz/mai
> >> lman/listinfo/wekalist
> >> > > List etiquette: http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.html
> >> >
> >> > _______________________________________________
> >> > Wekalist mailing list
> >> > Send posts to: [hidden email]
> >> > List info and subscription status: https://list.waikato.ac.nz/mai
> >> lman/listinfo/wekalist
> >> > List etiquette: http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.html
> >> >
> >> >
> >> >
> >> > --
> >> > Thanks & Regards
> >> > Bhupesh Rawat.
> >> > Ph.D Scholar
> >> > Department of Computer Science,Babasaheb Bhimrao Ambedkar University
> >> > Vidya Vihar,Rai Bareilly road(Lucknow)
> >> > Ph. No: +91-9897065948
> >> >
> >> > ............................................................
> >> ...............................................................
> >> > *A man is the best judge of himself and he has to pay the price for
> >> what he
> >> > does.*
> >> > ............................................................
> >> ...............................................................
> >> >
> >> >
> >> >
> >> > _______________________________________________
> >> > Wekalist mailing list
> >> > Send posts to: [hidden email]
> >> > List info and subscription status: https://list.waikato.ac.nz/mai
> >> lman/listinfo/wekalist
> >> > List etiquette: http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.html
> >>
> >> _______________________________________________
> >> Wekalist mailing list
> >> Send posts to: [hidden email]
> >> List info and subscription status: https://list.waikato.ac.nz/mai
> >> lman/listinfo/wekalist
> >> List etiquette: http://www.cs.waikato.ac.nz/~m
> >> l/weka/mailinglist_etiquette.html
> >>
> >
> >
> >
> > --
> > Thanks & Regards
> > Bhupesh Rawat.
> > Ph.D Scholar
> > Department of Computer Science,Babasaheb Bhimrao Ambedkar University
> > Vidya Vihar,Rai Bareilly road(Lucknow)
> > Ph. No: +91-9897065948
> >
> > ............................................................
> > ...............................................................
> > *A man is the best judge of himself and he has to pay the price for what
> > he
> > does.*
> > ............................................................
> > ...............................................................
> >
> >
> >
> >
>
>
> --
> Thanks & Regards
> Bhupesh Rawat.
> Ph.D Scholar
> Department of Computer Science,Babasaheb Bhimrao Ambedkar University
> Vidya Vihar,Rai Bareilly road(Lucknow)
> Ph. No: +91-9897065948
>
> ...........................................................................................................................
> *A man is the best judge of himself and he has to pay the price for what he
> does.*
> ...........................................................................................................................
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>
> ------------------------------
>
> _______________________________________________
> Wekalist mailing list
> [hidden email]
> https://list.waikato.ac.nz/mailman/listinfo/wekalist
>
>
> End of Wekalist Digest, Vol 172, Issue 44
> *****************************************
>
> _______________________________________________
> Wekalist mailing list
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