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Multi-objective optimisation based fuzzy association rule mining method

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Abstract

Fuzzy association rule mining (FARM) is a mainstream method to discover hidden patterns and association rules in quantitative data. It is essential to improve performance metrics, including quantity performance (e.g., the number of rules, the number of frequent itemsets) and quality performance (e.g., fuzzy support and confidence). The current approaches inadequately support optimisation of both quantity and quality performance. We propose a multi-objective optimisation algorithm for FARM (MOOFARM), where quantity and quality performance metrics are improved and validated simultaneously. The experimental evaluation conducted on a real dataset showcases the outstanding performance of MOOFARM against state-of-the-art works. In particular, at minimum support = 0.1, minimum confidence = 0.7, our MOOFARM increases the quantity performance up to 11 times. The proposed method improves the quality performance up to 71.05%.

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Acknowledgements

Supported by the National Key R&D Program of China (No. 2018YFB1003201), the National Natural Science Foundation of P. R. China (No. 61672296, No. 61872196, No. 61872194, and No. 61902196), Scientific and Technological Support Project of Jiangsu Province (No. BE2017166, and No. BE2019740), Major Natural Science Research Projects in Colleges and Universities of Jiangsu Province (No. 18KJA520008), Six Talent Peaks Project of Jiangsu Province (RJFW-111), and NUPTSF (No. NY220014, and No. NY220188).

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Authors and Affiliations

  1. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

    Hui Zheng, Jing He & Peng Li

  2. Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Jiangsu Province, Nanjing, China

    Hui Zheng & Peng Li

  3. Swinburne University and Technology, Melbourne, Australia

    Hui Zheng

  4. Software and Computational Systems Program, Data 61, CSIRO, Canberra, Australia

    Qing Liu

  5. School of Info Technology, Deakin University Burwood Campus, Melbourne, Australia

    Jianhua Li

  6. School of Information Technology, Deakin University, Geelong, Australia

    Guangli Huang

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  1. Hui Zheng
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  2. Jing He
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  3. Qing Liu
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  4. Jianhua Li
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  5. Guangli Huang
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  6. Peng Li
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Correspondence to Jing He or Peng Li.

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This article belongs to the Topical Collection: Special Issue on Web Intelligence = Artificial Intelligence in the Connected World

Guest Editors: Yuefeng Li, Amit Sheth, Athena Vakali, and Xiaohui Tao

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Zheng, H., He, J., Liu, Q. et al. Multi-objective optimisation based fuzzy association rule mining method. World Wide Web 26, 1055–1072 (2023). https://doi.org/10.1007/s11280-022-01073-8

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