Normalized weighted and reverse weighted correlation based Apriori algorithm

Abstract

Data mining, a need in the modern era of technology where data matters the most, is a prodigious role player. Among the existing techniques of data mining, Association rule is one of the most important tasks which is devoted to discover frequent itemsets and draw the correlations among the items in them. In the recent researches for association rule mining, different supporting threshold and pruning techniques have been inculcated in Apriori algorithm which is supposed to control the generation of frequent itemsets without neglecting any item that matters i.e. affects the transaction. Normalized weighted and reverse weighted correlation (NWRWC) based apriori algorithm is important for mining frequent and infrequent itemsets in a repository where items have different importance. Some researches have proposed the method of applying weights according to the importance of the items but in these methods many items with high supporting degree but low weight get pruned. NWRWC based Apriori algorithm is proposed to deal with this situation by applying direct normalized weights as well as reverse normalized weights to the items. It further establishes the relevance between itemsets using weighted correlation methods. Since not only frequent but also infrequent itemsets plays pivotal role in the association rule mining, both of them have been calculated using both weight and reverse weight. The experimental results demonstrate the efficiency and effectiveness of this approach. � 2015 IEEE.