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Association rule mining with fuzzy linguistic information based on attribute partial ordered structure

  • Foundation, algebraic, and analytical methods in soft computing
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Abstract

In many application domains, there is an urgent need for data owners to mine attribute associations hidden in linguistic conceptual knowledge. Numerous linguistically valued facts from the actual world have been modeled using the fuzzy linguistic approach. To solve the problem of association rule mining with fuzzy linguistic information, this paper proposes an association rule mining approach based on fuzzy linguistic attribute partial ordered structure diagram (FL-APOSD). First, complex relationships between linguistic values in association rule mining are represented by fuzzy linguistic association nodes and association paths via FL-APOSD. On this basis, the maximum frequent attribute set is mined from the FL-APOSD, and then the non-redundancy association rules are extracted. Second, to show the information hidden in the rules and help users to deeply understand the mining results, a fuzzy linguistic association rule visualization approach is proposed to convert the association rules into the FL-APOSD-based knowledge representation. Finally, experimental results on real-world datasets show the proposed approach’s high efficiency, outperforming two relevant state-of-the-art approaches.

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Funding

This work is supported by the National Natural Science Foundation of China (nos. 61976124, 62176142), the National Key R &D Program (no. 2018YFC1707703), and Special Foundation for Distinguished Professors of Shandong Jianzhu University.

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Authors and Affiliations

  1. School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China

    Kuo Pang & Mingyu Lu

  2. School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China

    Shaoxiong Li

  3. School of Computer Science and Technology, Dalian University of Technology, Dalian, 116081, China

    Yifan Lu

  4. Software engineering institute, East China Normal University, Shanghai, 200062, China

    Ning Kang

  5. School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China

    Li Zou

Authors
  1. Kuo Pang
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  2. Shaoxiong Li
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  3. Yifan Lu
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  4. Ning Kang
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  5. Li Zou
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  6. Mingyu Lu
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Contributions

KP: Conceptualization, Writing—original draft, Validation, Visualization. SL: Conceptualization, Validation. YL: Validation, Visualization. NK: Conceptualization, Investigation. LZ: Conceptualization, Methodology.ML: Investigation, Validation.

Corresponding authors

Correspondence to Li Zou or Mingyu Lu.

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Pang, K., Li, S., Lu, Y. et al. Association rule mining with fuzzy linguistic information based on attribute partial ordered structure. Soft Comput 27, 17447–17472 (2023). https://doi.org/10.1007/s00500-023-09145-1

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