Abstract
High-utility itemset mining (HUIM) is a critical issue in recent years since it can reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) or association-rule mining (ARM). In the past, a GA-based appraoch was designed to mine HUIs. It suffers, however, the combinational problem to assign the initial chromosomes for later evolution process. Besides, it is a non-trivial task to find the appropriate parameters for GA-based mechanism. In this paper, a binary PSO-based algorithm is thus proposed to efficiently find HUIs. A sigmoid function is adopted in the designed algorithm in the evolution process for discovering HUIs. Substantial experiments on real-life datasets show that the proposed algorithm has better results compared to the state-of-the-art GA-based algorithm of HUIM in terms of execution time and number of discovered HUIs.
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Lin, J.CW., Yang, L., Fournier-Viger, P., Wu, MT., Hong, TP., Wang, L.SL. (2015). A Swarm-Based Approach to Mine High-Utility Itemsets. In: Wang, L., Uesugi, S., Ting, IH., Okuhara, K., Wang, K. (eds) Multidisciplinary Social Networks Research. MISNC 2015. Communications in Computer and Information Science, vol 540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48319-0_48
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DOI: https://doi.org/10.1007/978-3-662-48319-0_48
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