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Hierarchical hesitant fuzzy K-means clustering algorithm

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Abstract

Due to the limitation and hesitation in one’s knowledge, the membership degree of an element to a given set usually has a few different values, in which the conventional fuzzy sets are invalid. Hesitant fuzzy sets are a powerful tool to treat this case. The present paper focuses on investigating the clustering technique for hesitant fuzzy sets based on the K-means clustering algorithm which takes the results of hierarchical clustering as the initial clusters. Finally, two examples demonstrate the validity of our algorithm.

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Authors and Affiliations

  1. School of Economics and Management, Southeast University, Nanjing, 211189, China

    Na Chen & Ze-shui Xu

  2. School of Applied Mathematics, Nanjing University of Finance and Economics, Nanjing, 210023, China

    Na Chen

  3. Business School, Sichuan University, Chengdu, 610064, China

    Ze-shui Xu

  4. School of Economics and Management, Tsinghua University, Beijing, 100084, China

    Mei-mei Xia

Authors
  1. Na Chen
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  2. Ze-shui Xu
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  3. Mei-mei Xia
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Corresponding author

Correspondence to Ze-shui Xu.

Additional information

Supported by the National Natural Science Foundation of China (61273209).

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Chen, N., Xu, Zs. & Xia, Mm. Hierarchical hesitant fuzzy K-means clustering algorithm. Appl. Math. J. Chin. Univ. 29, 1–17 (2014). https://doi.org/10.1007/s11766-014-3091-8

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  • DOI: https://doi.org/10.1007/s11766-014-3091-8

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