Weighted fuzzy c-means clustering based on double coding genetic algorithm

Authors:
Duo Chen;Du-Wu Cui;Chao-Xue Wang
Affiliations:
School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China;School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China;School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

Citing 7
Cited 1

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Abstract

We propose a double coding scheme in genetic algorithm (GA) and apply it to the fuzzy features-weighting clustering problems. Each individual consists of two segments of codes for cluster centers and feature weights. The two segments are evolved simultaneously in the clustering process. A modified clustering objective function is defined. A weighted fuzzy c-means operator and a feature weights learning operator are designed to guide computing cluster centers and feature weights in an individual respectively. On the basis of the above work, a novel weighed fuzzy c-means clustering algorithm based on double coding GA is advanced.