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Association Rule Mining through Combining Hybrid Water Wave Optimization Algorithm with Levy Flight

Author

Listed:
  • Qiyi He

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, China)

  • Jin Tu

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, China)

  • Zhiwei Ye

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, China)

  • Mingwei Wang

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, China)

  • Ye Cao

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, China)

  • Xianjing Zhou

    (Wuhan Zhuoer Information Technology Co., Ltd., Wuhan 430312, China)

  • Wanfang Bai

    (Xining Data Services Authority, Xining 810007, China)

Abstract

Association rule mining (ARM) is one of the most important tasks in data mining. In recent years, swarm intelligence algorithms have been effectively applied to ARM, and the main challenge has been to achieve a balance between search efficiency and the quality of the mined rules. As a novel swarm intelligence algorithm, the water wave optimization (WWO) algorithm has been widely used for combinatorial optimization problems, with the disadvantage that it tends to fall into local optimum solutions and converges slowly. In this paper, a novel hybrid ARM method based on WWO with Levy flight (LWWO) is proposed. The proposed method improves the solution of WWO by expanding the search space through Levy flight while effectively increasing the search speed. In addition, this paper employs the hybrid strategy to enhance the diversity of the population in order to obtain the global optimal solution. Moreover, the proposed ARM method does not generate frequent items, unlike traditional algorithms (e.g., Apriori), thus reducing the computational overhead and saving memory space, which increases its applicability in real-world business cases. Experiment results show that the performance of the proposed hybrid algorithms is significantly better than that of the WWO and LWWO in terms of quality and number of mined rules.

Suggested Citation

  • Qiyi He & Jin Tu & Zhiwei Ye & Mingwei Wang & Ye Cao & Xianjing Zhou & Wanfang Bai, 2023. "Association Rule Mining through Combining Hybrid Water Wave Optimization Algorithm with Levy Flight," Mathematics, MDPI, vol. 11(5), pages 1-19, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1195-:d:1083809
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    References listed on IDEAS

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    1. Clarisse Dhaenens & Laetitia Jourdan, 2022. "Metaheuristics for data mining: survey and opportunities for big data," Annals of Operations Research, Springer, vol. 314(1), pages 117-140, July.
    2. Yan, Zheping & Zhang, Jinzhong & Tang, Jialing, 2021. "Path planning for autonomous underwater vehicle based on an enhanced water wave optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 192-241.
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