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A platform for research: civil engineering, architecture and urbanism
Fuzzy Clustering Research Based on Intelligent Computing
FCM is sensitive to initialization and tends to result in local minimum in iterations. This paper studies the crossover and mutation probability of genetic algorithm and presents a new crossover and mutation probability. The proposed clustering scheme based on genetic algorithm and fuzzy c-means takes full advantage of the global optimization of genetic algorithm and the local search ability of FCM. The experiment results show that the proposed scheme has higher accuracy than traditional scheme.
Fuzzy Clustering Research Based on Intelligent Computing
FCM is sensitive to initialization and tends to result in local minimum in iterations. This paper studies the crossover and mutation probability of genetic algorithm and presents a new crossover and mutation probability. The proposed clustering scheme based on genetic algorithm and fuzzy c-means takes full advantage of the global optimization of genetic algorithm and the local search ability of FCM. The experiment results show that the proposed scheme has higher accuracy than traditional scheme.
Fuzzy Clustering Research Based on Intelligent Computing
Liu, Jun (author) / Wu, Xiaoli (author) / Luo, Xiaoyuan (author)
2015-12-01
157771 byte
Conference paper
Electronic Resource
English
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