Soft Computing

, Volume 21, Issue 17, pp 5103–5121 | Cite as

A binary PSO approach to mine high-utility itemsets

  • Jerry Chun-Wei Lin
  • Lu Yang
  • Philippe Fournier-Viger
  • Tzung-Pei Hong
  • Miroslav Voznak
Methodologies and Application


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.


Binary PSO OR/NOR-tree Discrete PSO High-utility itemsets TWU model 



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.

Compliance with ethical standards

Conflict of interest

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

Ethical approval

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


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Jerry Chun-Wei Lin
    • 1
  • Lu Yang
    • 1
  • Philippe Fournier-Viger
    • 2
  • Tzung-Pei Hong
    • 3
    • 4
  • Miroslav Voznak
    • 5
  1. 1.School of Computer Science and TechnologyHarbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina
  2. 2.School of Natural Sciences and HumanitiesHarbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina
  3. 3.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungTaiwan, ROC
  4. 4.Department of Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan, ROC
  5. 5.Department of Telecommunications, Faculty of Electrical Engineering and Computer ScienceVSB Technical University of OstravaOstrava-PorubaCzech Republic

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