Hesitant fuzzy agglomerative hierarchical clustering algorithms

Recently, hesitant fuzzy sets (HFSs) have been studied by many researchers as a powerful tool to describe and deal with uncertain data, but relatively, very few studies focus on the clustering analysis of HFSs. In this paper, we propose a novel hesitant fuzzy agglomerative hierarchical clustering al...

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Veröffentlicht in:International journal of systems science Jg. 46; H. 3; S. 562 - 576
Hauptverfasser: Zhang, Xiaolu, Xu, Zeshui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Taylor & Francis 17.02.2015
Schlagworte:
Agglomeration
agglomerative hierarchical clustering
Algorithms
Cluster analysis
Clustering
Clusters
Fuzzy
Fuzzy logic
Fuzzy set theory
hesitant fuzzy distance
hesitant fuzzy set
interval-valued hesitant fuzzy set
ISSN:0020-7721, 1464-5319
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Zusammenfassung:Recently, hesitant fuzzy sets (HFSs) have been studied by many researchers as a powerful tool to describe and deal with uncertain data, but relatively, very few studies focus on the clustering analysis of HFSs. In this paper, we propose a novel hesitant fuzzy agglomerative hierarchical clustering algorithm for HFSs. The algorithm considers each of the given HFSs as a unique cluster in the first stage, and then compares each pair of the HFSs by utilising the weighted Hamming distance or the weighted Euclidean distance. The two clusters with smaller distance are jointed. The procedure is then repeated time and again until the desirable number of clusters is achieved. Moreover, we extend the algorithm to cluster the interval-valued hesitant fuzzy sets, and finally illustrate the effectiveness of our clustering algorithms by experimental results.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0020-7721
1464-5319
DOI:10.1080/00207721.2013.797037