Mining correlated high-utility itemsets using various measures

Abstract

Discovering high-utility itemsets consists of finding sets of items that yield a high profit in customer transaction databases. An important limitation of traditional high-utility itemset mining is that only the utility measure is used for assessing the interestingness of patterns. This leads to finding several itemsets that have a high profit but contain items that are weakly correlated. To address this issue, this paper proposes to integrate the concept of correlation in high-utility itemset mining to find profitable itemsets that are highly correlated, using the all-confidence and bond measures. An efficient algorithm named FCHM (Fast Correlated High-utility itemset Miner) is proposed to efficiently discover correlated high-utility itemsets. Two versions of the algorithm are proposed, named FCHMall-confidence and FCHMbond based on the allconfidence and bond measures, respectively. An experimental evaluation was done using four real-life benchmark datasets from the high-utility itemset mining litterature: mushroom, retail, kosarak and foodmart. Results show that FCHM is efficient and can prune a huge amount of weakly correlated high-utility itemsets.