Computer Science ›› 2021, Vol. 48 ›› Issue (1): 145-151.doi: 10.11896/jsjkx.200400043

• Database & Big Data & Data Science • Previous Articles     Next Articles

Weighted Hesitant Fuzzy Clustering Based on Density Peaks

ZHANG Yu, LU Yi-hong, HUANG De-cai   

  1. College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2020-04-10 Revised:2020-08-08 Online:2021-01-15 Published:2021-01-15
  • About author:ZHANG Yu,born in 1994,postgra-duate,is a member of China Computer Federation.Her main research interests include data mining and so on.
    LU Yi-hong,born in 1968,master,associate professor,is a member of China Computer Federation.Her main research interests include software theory and data mining.
  • Supported by:
    Zhejiang Public Welfare Technology Research Project(LGG19E090001).

Abstract: Due to cognitive limitations and the information uncertainty,traditional fuzzy clustering cannot effectively solve the decision-making problems in a real-life scenario when cluster analysis is carried out on the decision problem.Therefore,hesitant fuzzy sets(HFSs) clustering algorithms were proposed.The conception of hesitant fuzzy sets is evolved from fuzzy sets which are applied to fuzzy linguistic approach.The distance function of the hierarchical hesitant fuzzy K-means clustering algorithm has the same weight since the datasets information is seldom considered,and the computational complexity for computing the cluster center is exponential which is unavailable in the big data environment.In order to solve the above problems,this paper presents a novel clustering algorithm for hesitant fuzzy sets based on density peaks,called WHFDP.Firstly,a new method for extending the short hesitant fuzzy elements set to calculate the distance between two HFSs is proposed and a new formula for calculating the weight of distance function combined with the coefficient of variation is given.In addition,the computational complexity for computing the cluster center is reduced by using density peaks clustering method to select cluster center.Meanwhile,the adaptability to data sets with different sizes and arbitrary shapes is also improved.The time complexity and space complexity of the algorithm are reduced to polynomial level.Finally,typical data sets are used for simulation experiments,which prove the effectiveness of the new algorithm.

Key words: Clustering algorithm, Coefficient of variation, Data mining, Density peaks, Hesitant fuzzy sets

CLC Number: 

  • TP391
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