I and my friend have read the paper about FURIA, but we are still confused.
After reading the code java of FURIA, so many rows. T_T
We got exercise from my lecture if we use and apply this algorithm, we must know and understand well about furia.
After looking more paper, unfortunately, we are confused again.
May be, you can help us, there are some question :
- Please explain about the step from input dataset until output rule
- Dataset is partitioned by growing and pruning data, default 3 fold. Is it true that 2 fold for growing data (training data) and 1 fold for pruning data (testing data). Then how the model when use cross validation 10 fold ?
- What the function Apriori distribution there, is about count frequent itemset, to prepare the candidate rule ?
- Then, how calculate the purity or count and processing the fuzzification phase ?
- Then, how count the CF in final step. How count confidence from the rule?
Your explaination is helpful.
On Sun, May 21, 2017 at 4:08 AM, <[hidden email]> wrote:
Send Wekalist mailing list submissions to
Salam,Betha Nurina Sari
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
|Free forum by Nabble||Edit this page|