Efficient mining of high-utility itemsets using multiple minimum utility thresholds

摘要

In the field of data mining, the topic of high-utility itemset mining (HUIM) has recently gained a lot of attention from researchers as it takes many factors into account that are useful for decision-making by retail managers. In the past, many algorithms have been presented for HUIM but most of them suffer from the limitation of using a single minimum utility threshold to identify high-utility itemsets (HUIs). For real-life applications, finding itemsets using a single threshold is inadequate and unfair since each item is different. Hence, the diversity or importance of each item should be considered. This paper proposes a solution to this issue by defining the novel task of HUIM with multiple minimum utility thresholds (named as HUIM-MMU). This task lets users specify a different minimum utility threshold for each item to identify more useful and specific HUls, which would generate more profits when compared to HUIs discovered based on a single minimum utility threshold. The HUI-MMU algorithm is designed to mine Hills in a level-wise manner. The sorted downward closure (SDC) property and the least minimum utility (LMU) concept are developed to avoid a combinatorial explosion for identifying HUIs and to ensure the completeness and correctness of HUI-MMU for discovering HUls. Meanwhile, two improved algorithms, namely HUI-MMUTID and HUI-MMUTE, are presented based on the TID-index and EUCP strategies. Those strategies can be used to speed up the mining performance to discover HUls. Substantial experiments on both real-life and synthetic datasets show that the designed algorithms can efficiently and effectively discover the complete set of HUIs in databases by considering multiple minimum utility thresholds. (C) 2016 Elsevier B.V. All rights reserved.