GHFHC: Generalized Hesitant Fuzzy Hierarchical Clustering Algorithm

Dealing with uncertainty is an undeniable challenge in the real‐world problems. In this paper, we focus on hesitant environment such as generalized hesitant fuzzy sets introduced by Qian et al. So we propose a new generalized hesitant fuzzy hierarchical clustering (GHFHC) algorithm based on Atanasso...

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Vydané v:International journal of intelligent systems Ročník 31; číslo 9; s. 855 - 871
Hlavní autori: Aliahmadipour, Laya, Eslami, Esfandiar
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Blackwell Publishing Ltd 01.09.2016
John Wiley & Sons, Inc
Predmet:
Algorithms
Cluster analysis
Clusters
Crisps
Fuzzy
Fuzzy set theory
Fuzzy sets
Intelligent systems
Run time (computers)
ISSN:0884-8173, 1098-111X
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Shrnutí:Dealing with uncertainty is an undeniable challenge in the real‐world problems. In this paper, we focus on hesitant environment such as generalized hesitant fuzzy sets introduced by Qian et al. So we propose a new generalized hesitant fuzzy hierarchical clustering (GHFHC) algorithm based on Atanassov's intuitionistic fuzzy set theory. We extend conventional hierarchical clustering, which just works on the crisp data, and introduce a clustering algorithm, which can be applied on large data set with generalized hesitant fuzzy data. The run time of the GHFHC algorithm shows that its computational complexity will be low. Also, the GHFHC algorithm produces the clusters with arbitrary shapes by using the various distance measures. Finally, an example is provided to illustrate the practicality of the proposed algorithm.
Bibliografia:ark:/67375/WNG-3QPVTL6Q-T
istex:F6461E84237EB0A26F6ABCB97F572900D58FFF41
ArticleID:INT21807
This article was originally published on 21 January 2016. Subsequently an error in the author's affiliation has been corrected and the corrected article was published on 28 January 2016.
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ISSN:0884-8173
1098-111X
DOI:10.1002/int.21807