Improved possibilistic C-means clustering algorithms

Jiang She Zhang; Yiu Wing Leung

doi:10.1109/TFUZZ.2004.825079

Improved possibilistic C-means clustering algorithms

Jiang She Zhang^*
, Yiu Wing Leung

^*Corresponding author for this work

Department of Computer Science

Research output: Contribution to journal › Journal article › peer-review

172 Citations (Scopus)

Abstract

A possibilistic approach was proposed in a previous paper for C-means clustering, and two algorithms realizing this approach were reported in two previous papers. Although the possibilistic approach is sound, these two algorithms tend to find identical clusters. In this paper, we modify and improve these algorithms to overcome their shortcoming. The numerical results demonstrate that the improved algorithms can determine proper clusters and they can realize the advantages of the possibilistic approach.

Original language	English
Pages (from-to)	209-217
Number of pages	9
Journal	IEEE Transactions on Fuzzy Systems
Volume	12
Issue number	2
DOIs	https://doi.org/10.1109/TFUZZ.2004.825079
Publication status	Published - Apr 2004

User-Defined Keywords

Approach
C-means
Clustering
Possibilistic

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10.1109/TFUZZ.2004.825079

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title = "Improved possibilistic C-means clustering algorithms",

abstract = "A possibilistic approach was proposed in a previous paper for C-means clustering, and two algorithms realizing this approach were reported in two previous papers. Although the possibilistic approach is sound, these two algorithms tend to find identical clusters. In this paper, we modify and improve these algorithms to overcome their shortcoming. The numerical results demonstrate that the improved algorithms can determine proper clusters and they can realize the advantages of the possibilistic approach.",

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note = "Funding Information: Manuscript received February 28, 2000; revised July 13, 2001, September 18, 2002, and July 14, 2003. This work was supported by the Hong Kong Baptist University Faculty Research Grant, NSFC Project 60075001, and High-Tech R\&D (863) 2001AA113 182. J.-S. Zhang is with the Department of Information Science, Xi{\textquoteright}an Jiaotong University, 710049 Xi{\textquoteright}an, China. Y.-W. Leung is with the Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong. Digital Object Identifier 10.1109/TFUZZ.2004.825079",

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N2 - A possibilistic approach was proposed in a previous paper for C-means clustering, and two algorithms realizing this approach were reported in two previous papers. Although the possibilistic approach is sound, these two algorithms tend to find identical clusters. In this paper, we modify and improve these algorithms to overcome their shortcoming. The numerical results demonstrate that the improved algorithms can determine proper clusters and they can realize the advantages of the possibilistic approach.

AB - A possibilistic approach was proposed in a previous paper for C-means clustering, and two algorithms realizing this approach were reported in two previous papers. Although the possibilistic approach is sound, these two algorithms tend to find identical clusters. In this paper, we modify and improve these algorithms to overcome their shortcoming. The numerical results demonstrate that the improved algorithms can determine proper clusters and they can realize the advantages of the possibilistic approach.

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KW - Clustering

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DO - 10.1109/TFUZZ.2004.825079

M3 - Journal article

AN - SCOPUS:1942532748

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JO - IEEE Transactions on Fuzzy Systems

JF - IEEE Transactions on Fuzzy Systems

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Improved possibilistic C-means clustering algorithms

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