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A binary PSO approach to mine high-utility itemsets

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Abstract

High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) or association-rule mining (ARM). Several algorithms have been presented to mine high-utility itemsets (HUIs) and most of them have to handle the exponential search space for discovering HUIs when the number of distinct items and the size of database are very large. In the past, a heuristic HUPE\( _\mathrm{umu}\)-GRAM algorithm was proposed to mine HUIs based on genetic algorithm (GA). For the evolutionary computation (EC) techniques of particle swarm optimization (PSO), it only requires fewer parameters compared to the GA-based approaches. Since the traditional PSO mechanism is used to handle the continuous problem, in this paper, the discrete PSO is adopted to encode the particles as the binary variables. An efficient PSO-based algorithm, namely HUIM-BPSO, is proposed to efficiently find HUIs. The designed HUIM-BPSO algorithm finds the high-transaction-weighted utilization 1-itemsets (1-HTWUIs) as the size of the particles based on transaction-weighted utility (TWU) model, which can greatly reduce the combinational problem in evolution process. The sigmoid function is adopted in the updating process of the particles for the designed HUIM-BPSO algorithm. An OR/NOR-tree structure is further developed to reduce the invalid combinations for discovering HUIs. Substantial experiments on real-life datasets show that the proposed algorithm outperforms the other heuristic algorithms for mining HUIs in terms of execution time, number of discovered HUIs, and convergence.

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Acknowledgments

This research was partially supported by the Shenzhen Peacock Project, China, under Grant KQC201109020055A, by the National Natural Science Foundation of China (NSFC) under Grant No. 61503092, by the Natural Scientific Research Innovation Foundation in Harbin Institute of Technology under Grant HIT.NSRIF.2014100, and by the Shenzhen Strategic Emerging Industries Program under Grant ZDSY20120613125016389.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Jerry Chun-Wei Lin & Lu Yang

  2. School of Natural Sciences and Humanities, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Philippe Fournier-Viger

  3. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, ROC

    Tzung-Pei Hong

  4. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, ROC

    Tzung-Pei Hong

  5. Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Miroslav Voznak

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  1. Jerry Chun-Wei Lin
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  2. Lu Yang
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  3. Philippe Fournier-Viger
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  4. Tzung-Pei Hong
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  5. Miroslav Voznak
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Correspondence to Jerry Chun-Wei Lin.

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The authors declare that there are no conflicts of interest in this paper.

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This article does not contain any studies with human participants performed by any of the authors.

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Communicated by V. Loia.

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Lin, J.CW., Yang, L., Fournier-Viger, P. et al. A binary PSO approach to mine high-utility itemsets. Soft Comput 21, 5103–5121 (2017). https://doi.org/10.1007/s00500-016-2106-1

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  • DOI: https://doi.org/10.1007/s00500-016-2106-1

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