Real-coded Genetic Optimization of Fuzzy Clustering
نویسندگان
چکیده
A genetic approach is developed, which is suitable for the optimization of fuzzy c-means clustering. The approach is based on real encoding of the prototype variables (cluster centers) and uses appropriate genetic operators and techniques to optimize the clustering criterion. Experimental results concerning diicult clustering problems show that the proposed approach is very successful in generating fuzzy partitions and prototypes and outperforms the fuzzy c-means algorithm in terms of the correct placement of patterns into partitions.
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