High Utility Rare Itemset Mining over Transaction Databases

Abstract

High-Utility Rare Itemset (HURI) mining finds itemsets from a database which have their utility no less than a given minimum utility threshold and have their support less than a given frequency threshold. Identifying high-utility rare itemsets from a database can help in better business decision making by highlighting the rare itemsets which give high profits so that they can be marketed more to earn good profit. Some two-phase algorithms have been proposed to mine high-utility rare itemsets. The rare itemsets are generated in the first phase and the high-utility rare itemsets are extracted from rare itemsets in the second phase. However, a two-phase solution is inefficient as the number of rare itemsets is enormous as they increase at a very fast rate with the increase in the frequency threshold. In this paper, we propose an algorithm, namely UP-Rare Growth, which uses UP-Tree data structure to find high-utility rare itemsets from a transaction database. Instead of finding the rare itemsets explicitly, our proposed algorithm works on both frequency and utility of itemsets together. We also propose a couple of effective strategies to avoid searching the non-useful branches of the tree. Extensive experiments show that our proposed algorithm outperforms the state-of-the-art algorithms in terms of number of candidates.