A fuzzy clustering algorithm based on evolutionary programming

Authors:
Hongbin Dong;Yuxin Dong;Cheng Zhou;Guisheng Yin;Wei Hou
Affiliations:
National Science Park, Harbin Engineering University, Harbin 150001, China and Department of Computer Science, Harbin Normal University, Harbin 150080, China;National Science Park, Harbin Engineering University, Harbin 150001, China;National Science Park, Harbin Engineering University, Harbin 150001, China;National Science Park, Harbin Engineering University, Harbin 150001, China;National Science Park, Harbin Engineering University, Harbin 150001, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

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.