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Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice

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  • Shuangsheng Wu

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Jie Lin

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Zhenyu Zhang

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Yushu Yang

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

Abstract

The fuzzy clustering algorithm has become a research hotspot in many fields because of its better clustering effect and data expression ability. However, little research focuses on the clustering of hesitant fuzzy linguistic term sets (HFLTSs). To fill in the research gaps, we extend the data type of clustering to hesitant fuzzy linguistic information. A kind of hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm is proposed. Furthermore, we propose a hesitant fuzzy linguistic Boole matrix clustering algorithm and compare the two clustering algorithms. The proposed clustering algorithms are applied in the field of judicial execution, which provides decision support for the executive judge to determine the focus of the investigation and the control. A clustering example verifies the clustering algorithm’s effectiveness in the context of hesitant fuzzy linguistic decision information.

Suggested Citation

  • Shuangsheng Wu & Jie Lin & Zhenyu Zhang & Yushu Yang, 2021. "Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice," Mathematics, MDPI, vol. 9(4), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:4:p:370-:d:498641
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    References listed on IDEAS

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    1. Liang, Gin-Shuh & Chou, Tsung-Yu & Han, Tzeu-Chen, 2005. "Cluster analysis based on fuzzy equivalence relation," European Journal of Operational Research, Elsevier, vol. 166(1), pages 160-171, October.
    2. Hua Zhao & Zeshui Xu & Zhong Wang, 2013. "Intuitionistic Fuzzy Clustering Algorithm Based On Boole Matrix And Association Measure," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 95-118.
    3. Xu, Zeshui, 2005. "Deviation measures of linguistic preference relations in group decision making," Omega, Elsevier, vol. 33(3), pages 249-254, June.
    4. Bai, Chunguang & Dhavale, Dileep & Sarkis, Joseph, 2016. "Complex investment decisions using rough set and fuzzy c-means: An example of investment in green supply chains," European Journal of Operational Research, Elsevier, vol. 248(2), pages 507-521.
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    Cited by:

    1. Hui Chen & Kunpeng Xu & Lifei Chen & Qingshan Jiang, 2021. "Self-Expressive Kernel Subspace Clustering Algorithm for Categorical Data with Embedded Feature Selection," Mathematics, MDPI, vol. 9(14), pages 1-22, July.
    2. Anqi Yang & Shudong Yang, 2023. "The Impact of the Implementation of International Law on Marine Environmental Protection on International Public Health Driven by Multi-Source Network Comment Mining," IJERPH, MDPI, vol. 20(6), pages 1-16, March.

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