Hierarchical hesitant fuzzy K-means clustering algorithm

  • Na Chen
  • Ze-shui XuEmail author
  • Mei-mei Xia


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.


Hesitant fuzzy set hierarchical clustering K-means clustering intuitionisitc fuzzy set 

MR Subject Classification

90B50 68T10 62H30 


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Copyright information

© Editorial Committee of Applied Mathematics-A Journal of Chinese Universities and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Economics and ManagementSoutheast UniversityNanjingChina
  2. 2.School of Applied MathematicsNanjing University of Finance and EconomicsNanjingChina
  3. 3.Business SchoolSichuan UniversityChengduChina
  4. 4.School of Economics and ManagementTsinghua UniversityBeijingChina

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