Binary partition for itemsets expansion in mining high utility itemsets

Abstract

High utility itemset mining has recently emerged to address the limitations of frequent itemset mining. It entails relevance measures to reflect both statistical significance and user expectations. Whether breadth-first or depth-first search algorithms are employed, most methods generate new candidates by 1-extension of existing itemsets (i.e., by adding only one item to verified itemsets to generate new potential candidates). As an alternative to 1-extension, we introduce an expansion method based on binary partition. We then define the transaction utility list and key support count and discuss a new pruning strategy. Based on the new itemset expansion method and pruning strategy, we propose an efficient high utility itemset mining algorithm called BPHUI-Mine (Binary Partition-based High Utility Itemsets Mine). Tests on publicly available datasets show that the proposed algorithm outperforms other state-of-the-art algorithms.