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

Qiyi He, Jin Tu, Zhiwei Ye (), Mingwei Wang, Ye Cao, Xianjing Zhou and Wanfang Bai
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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

Mathematics, 2023, vol. 11, issue 5, 1-19

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.

Keywords: data mining; association rule mining; water wave optimization algorithm; hybrid algorithm; Levy flight (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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