Paper Details

Association rule mining and classification are two major task of data mining. Theyare attracted wide attention in both research and application area recently. I propose a methodfor classification rules from multi-label dataset using association rule analysis. Multi labeldataset contains multiple class label attribute for predict target variable. We classify thatattribute using different approaches like naviye-baies, decision tree, Back propagation,Neural based classification and association rule based classification. Finding association rulefrom dataset we have to apply various algorithms like Apriori, Fp-Growth, etc. I proposedFp-Growth algorithm for finding association rule from dataset because of Fp-Growth is animproved algorithm of Apriori and Fp-Growth is more efficient than Apriori. The number ofassociations present in even moderate sized databases can be, however, very large – usuallytoo large to be applied directly for classification purposes. Therefore, any classificationlearner using association rules has to perform three major steps: Mining a set of potentiallyaccurate rules, evaluating and pruning rules, and classifying future instances using the foundrule set. Implementation of improved Fp-Growth algorithm gives accurate and classify rule.This approach is more effective, accurate and efficient than other tradition algorithms.

Kanu Patel, Niki Kapadia, Mehul Parikh, “Discover Multi-Label Classification using Association Rule Mining”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 1, Issue 1, January 2014.