Efficient Mining of High-utility Itemsets Using Multiple Minimum Utility Thresholds

Authors

Jerry Chun-Wei Lin, School of Computer Science and TechnologyFollow
Wensheng Gan, School of Computer Science and TechnologyFollow
Philippe Fournier-Viger, Harbin Institute of TechnologyFollow
Tzung-Pei Hong, National Sun Yat-sen UniversityFollow
Justin Zhan, University of Nevada, Las VegasFollow

Document Type

Article

Publication Date

1-1-2016

Publication Title

Knowledge-Based Systems

Volume

113

First page number:

100

Last page number:

115

Abstract

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 HUIs, 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 HUIs 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 HUIs. 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 HUIs. 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. © 2016 Elsevier B.V.

Keywords

HUI-MMU; HUIM; LMU; Multiple thresholds; Sorted downward closure

Language

English

Repository Citation

Lin, J. C., Gan, W., Fournier-Viger, P., Hong, T., Zhan, J. (2016). Efficient Mining of High-utility Itemsets Using Multiple Minimum Utility Thresholds. Knowledge-Based Systems, 113 100-115.
http://dx.doi.org/10.1016/j.knosys.2016.09.013