24 September 2001 Weighting exponent m in fuzzy C-means (FCM) clustering algorithm
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Proceedings Volume 4554, Object Detection, Classification, and Tracking Technologies; (2001) https://doi.org/10.1117/12.441637
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China
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].
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jihong Pei, Jihong Pei, Xuan Yang, Xuan Yang, Xinbo Gao, Xinbo Gao, Weixing Xie, Weixing Xie, "Weighting exponent m in fuzzy C-means (FCM) clustering algorithm", Proc. SPIE 4554, Object Detection, Classification, and Tracking Technologies, (24 September 2001); doi: 10.1117/12.441637; https://doi.org/10.1117/12.441637

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