New algorithms for solving the fuzzy clustering problem

Mohamed S. Kamel; Shokri Z. Selim

doi:10.1016/0031-3203(94)90118-X

New algorithms for solving the fuzzy clustering problem

Mohamed S. Kamel^*
, Shokri Z. Selim

^*Corresponding author for this work

King Fahd University of Petroleum & Minerals

Research output: Contribution to journal › Article › peer-review

134 Scopus citations

Abstract

Two new algorithms for fuzzy clustering are presented. Convergence of the proposed algorithms is proved. An empirical study of their convergence behavior is discussed. The performance of the new algorithms is compared with the fuzzy c-means algorithm by testing them on four published data sets. Experimental results show that the new algorithms are faster and lead to computational savings.

Original language	English
Pages (from-to)	421-428
Number of pages	8
Journal	Pattern Recognition
Volume	27
Issue number	3
DOIs	https://doi.org/10.1016/0031-3203(94)90118-X
State	Published - Mar 1994

Keywords

Cluster analysis
Fuzzy c-means algorithm
Fuzzy clustering
Pattern recognition

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/0031-3203(94)90118-X

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title = "New algorithms for solving the fuzzy clustering problem",

abstract = "Two new algorithms for fuzzy clustering are presented. Convergence of the proposed algorithms is proved. An empirical study of their convergence behavior is discussed. The performance of the new algorithms is compared with the fuzzy c-means algorithm by testing them on four published data sets. Experimental results show that the new algorithms are faster and lead to computational savings.",

keywords = "Cluster analysis, Fuzzy c-means algorithm, Fuzzy clustering, Pattern recognition",

author = "Kamel, \{Mohamed S.\} and Selim, \{Shokri Z.\}",

year = "1994",

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AU - Selim, Shokri Z.

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N2 - Two new algorithms for fuzzy clustering are presented. Convergence of the proposed algorithms is proved. An empirical study of their convergence behavior is discussed. The performance of the new algorithms is compared with the fuzzy c-means algorithm by testing them on four published data sets. Experimental results show that the new algorithms are faster and lead to computational savings.

AB - Two new algorithms for fuzzy clustering are presented. Convergence of the proposed algorithms is proved. An empirical study of their convergence behavior is discussed. The performance of the new algorithms is compared with the fuzzy c-means algorithm by testing them on four published data sets. Experimental results show that the new algorithms are faster and lead to computational savings.

KW - Cluster analysis

KW - Fuzzy c-means algorithm

KW - Fuzzy clustering

KW - Pattern recognition

UR - https://www.scopus.com/pages/publications/0028667331

U2 - 10.1016/0031-3203(94)90118-X

DO - 10.1016/0031-3203(94)90118-X

M3 - Article

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SP - 421

EP - 428

JO - Pattern Recognition

JF - Pattern Recognition

IS - 3

ER -

New algorithms for solving the fuzzy clustering problem

Abstract

Keywords

ASJC Scopus subject areas

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