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A fuzzy clustering algorithm based on evolutionary programming
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DOI：10.1016/j.eswa.2009.04.031.png)
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In this paper, a fuzzy clustering method based on evolutionary programming (EPFCM) is proposed. The algorithm benefits from the global search strategy of evolutionary programming, to improve fuzzy c-means algorithm (FCM). The cluster validity can be measured by some cluster validity indices. To increase the convergence speed of the algorithm, we exploit the modified algorithm to change the number of cluster centers dynamically. Experiments demonstrate EPFCM can find the proper number of clusters, and the result of clustering does not depend critically on the choice of the initial cluster centers. The probability of trapping into the local optima will be very lower than FCM. (C) 2009 Elsevier Ltd. All rights reserved.
Keyword:
Fuzzy c-means algorithm
Evolutionary programming
Cluster validity
EPFCM
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IF:
7.5
论文数:
2.9W
被引数:
10.2W
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A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification若干模糊聚类有效性指标、遗传聚类及其在像元分类中的应用研究
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