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Association Rule Mining Based on Hybrid Whale Optimization Algorithm

Author

Listed:
  • Zhiwei Ye

    (Hubei University of Technology, China & Fujian Provincial Key Laboratory of Data Intensive Computing, China & Key Laboratory of Intelligent Computing and Information Processing, China)

  • Wenhui Cai

    (Hubei University of Technology, China)

  • Mingwei Wang

    (Hubei University of Technology, China)

  • Aixin Zhang

    (Hubei University of Technology, China)

  • Wen Zhou

    (Hubei University of Technology, China)

  • Na Deng

    (Hubei University of Technology, China)

  • Zimei Wei

    (Hubei University of Technology, China)

  • Daxin Zhu

    (Quanzhou Normal University, China & Fujian Provincial Key Laboratory of Data Intensive Computing, China & Key Laboratory of Intelligent Computing and Information Processing, China)

Abstract

Association Rule Mining(ARM) is one of the most significant and active research areas in data mining. Recently, Whale Optimization Algorithm (WOA) has been successfully applied in the field of data mining, however, it easily falls into the local optimum. Therefore, an improved WOA based adaptive parameter strategy and Levy Flight mechanism (LWOA) is applied to mine association rules. Meanwhile, a hybrid strategy that blends two algorithms to balance the exploration and exploitation phases is put forward, that is, grey wolf optimization algorithm (GWO), artificial bee colony algorithm (ABC) and cuckoo search algorithm (CS) are devoted to improving the convergence of LWOA. The approach performs a global search and finds the association rules sets by modeling the rule mining task as a multi-objective problem that simultaneously meets support, confidence, lift, and certain factor, which is examined on multiple data sets. Experimental results verify that the proposed method has better mining performance compared to other algorithms involved in the paper.

Suggested Citation

  • Zhiwei Ye & Wenhui Cai & Mingwei Wang & Aixin Zhang & Wen Zhou & Na Deng & Zimei Wei & Daxin Zhu, 2022. "Association Rule Mining Based on Hybrid Whale Optimization Algorithm," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 18(1), pages 1-22, January.
    • Handle: RePEc:igg:jdwm00:v:18:y:2022:i:1:p:1-22
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    References listed on IDEAS

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    1. Latha Banda & Karan Singh & Le Hoang Son & Mohamed Abdel-Basset & Pham Huy Thong & Hiep Xuan Huynh & David Taniar, 2020. "Recommender Systems Using Collaborative Tagging," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 16(3), pages 183-200, July.
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    Cited by:

    1. Zhang, Jinzhong & Zhang, Gang & Kong, Min & Zhang, Tan & Wang, Duansong & Chen, Rui, 2023. "CWOA: A novel complex-valued encoding whale optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 151-188.

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