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Novel Distance Measure for Hesitant Fuzzy Sets and Its Application to K-Means Clustering

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  • Feng Yan

    (Hunan Institute of Science and Technology, China)

  • Xiaoqiang Zhou

    (Hunan Institute of Science and Technology, China)

  • Yongzhi Wang

    (Hunan Institute of Science and Technology, China)

  • Li Chen

    (Hunan Institute of Science and Technology, China)

  • Wu Li

    (Hunan Institute of Science and Technology, China)

Abstract

Distance measures have recently been studied in-depth within the context of hesitant fuzzy sets. The authors analyze existing research on the distance measures of hesitant fuzzy sets and identify several limitations. This paper proposes a new distance measure for hesitant fuzzy sets to overcome these shortcomings. First, a new hesitance degree with better accuracy and applicability is defined. Then, a new method for measuring the distance between hesitant fuzzy sets is proposed by considering the hesitance degree. On this basis, an improved hesitant fuzzy K-means clustering algorithm is introduced to classify hesitant fuzzy sets. Finally, an example is given to illustrate the specific implementation process of the clustering method, and a comparative study on the example is conducted.

Suggested Citation

  • Feng Yan & Xiaoqiang Zhou & Yongzhi Wang & Li Chen & Wu Li, 2022. "Novel Distance Measure for Hesitant Fuzzy Sets and Its Application to K-Means Clustering," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 11(1), pages 1-32, January.
    • Handle: RePEc:igg:jfsa00:v:11:y:2022:i:1:p:1-32
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