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Proceedings Paper

Weighting exponent m in fuzzy C-means (FCM) clustering algorithm
Author(s): Jihong Pei; Xuan Yang; Xinbo Gao; Weixing Xie
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Paper Abstract

The weighting exponent m is an important parameter in fuzzy c-means (FCM) algorithm. In this paper, three basic problems about m in FCM algorithm: clustering validity method based on optimal m (or whether does optimal m exist), how does m effect on the performance of fuzzy clustering, and which is the proper range of m in general applications, are studied with the knee of objective function Jm, and fuzzy decision-making methods. Numerical experimental results show that the optimal m* for specific data set does exist. Moreover, a group of numerical experimental results indicate that, within the range of m (epsilon) (1.5, 3.5), the optimal m* monotone increase linearly against the separability (rho) of data set. So in practical applications, one can choose the value of m within the range of [1.5, 3.5].

Paper Details

Date Published: 24 September 2001
PDF: 6 pages
Proc. SPIE 4554, Object Detection, Classification, and Tracking Technologies, (24 September 2001); doi: 10.1117/12.441637
Show Author Affiliations
Jihong Pei, Xidian Univ. (China)
Xuan Yang, Xidian Univ. (China)
Xinbo Gao, Xidian Univ. (China)
Weixing Xie, Shen Zhen Univ. (China)

Published in SPIE Proceedings Vol. 4554:
Object Detection, Classification, and Tracking Technologies
Jun Shen; Sharatchandra Pankanti; Runsheng Wang, Editor(s)

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