Efficient Mining of High Average-Utility Itemsets with Multiple Minimum Thresholds

Abstract

High average-utility itemsets mining (HAUIM) is a key data mining task, which aims at discovering high average-utility itemsets (HAUIs) by taking itemset length into account in transactional databases. Most of these algorithms only consider a single minimum utility threshold for identifying the HAUIs. In this paper, we address this issue by introducing the task of mining HAUIs with multiple minimum average-utility thresholds (HAUIM-MMAU), where the user may assign a distinct minimum average-utility threshold to each item or itemset. Two efficient IEUCP and PBCS strategies are designed to further reduce the search space of the enumeration tree, and thus speed up the discovery of HAUIs when considering multiple minimum average utility thresholds. Extensive experiments carried on both real-life and synthetic databases show that the proposed approaches can efficiently discover the complete set of HAUIs when considering multiple minimum average-utility thresholds.