| A dynamic fuzzy clustering method based on genetic algorithm |
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| 作者姓名： | ZHENG Yan ZHOU Chunguang LIANG Yanchun GUO Dongwei |
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| 作者单位： | College of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China,College of Computer Science and Technology, Jilin University, Changchun 130012, China,College of Computer Science and Technology, Jilin University, Changchun 130012, China,College of Computer Science and Technology, Jilin University, Changchun 130012, China |
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| 基金项目： | Supported by the National Natural Science Foundation of China (Grant No. 60175024), the Key Project of Chinese Ministry of Education (No. 02090) and the Key Laboratory for Symbol Computation and Knowledge Engineering of Chinese Ministry of Education |
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| 摘 要： | A dynamic fuzzy clustering method is presented based on the genetic algorithm. By calculating the fuzzy dissimilarity between samples the essential associations among samples are modeled factually. The fuzzy dissimilarity between two samples is mapped into their Euclidean distance, that is, the high dimensional samples are mapped into the two-dimensional plane. The mapping is optimized globally by the genetic algorithm, which adjusts the coordinates of each sample, and thus the Euclidean distance, to approximate to the fuzzy dissimilarity between samples gradually. A key advantage of the proposed method is that the clustering is independent of the space distribution of input samples, which improves the flexibility and visualization. This method possesses characteristics of a faster convergence rate and more exact clustering than some typical clustering algorithms. Simulated experiments show the feasibility and availability of the proposed method.
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| 关 键 词： | dynamic fuzzy clustering fuzzy dissimilarity matrix genetic algorithm fuzzy c-means clustering |
A dynamic fuzzy clustering method based on genetic algorithm |
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| ZHENG Yan,ZHOU Chunguang,LIANG Yanchun,GUO Dongwei.A dynamic fuzzy clustering method based on genetic algorithm[J].Progress in Natural Science,2003,13(12):932-935. |
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| Authors: | ZHENG Yan Zhou Chunguang LIANG Yanchun GUO Dongwei |
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| Institution: | 1. College of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
2. College of Computer Science and Technology, Jilin University, Changchun 130012, China |
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| Abstract: | A dynamic fuzzy clustering method is presented based on the genetic algorithm. By calculating the fuzzy dissimilarity between samples the essential associations among samples are modeled factually. The fuzzy dissimilarity between two samples is mapped into their Euclidean distance, that is, the high dimensional samples are mapped into the two-dimensional plane. The mapping is optimized globally by the genetic algorithm, which adjusts the coordinates of each sample, and thus the Euclidean distance, to approximate to the fuzzy dissimilarity between samples gradually. A key advantage of the proposed method is that the clustering is independent of the space distribution of input samples, which improves the flexibility and visualization. This method possesses characteristics of a faster convergence rate and more exact clustering than some typical clustering algorithms. Simulated experiments show the feasibility and availability of the proposed method. |
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| Keywords: | dynamic fuzzy clustering fuzzy dissimilarity matrix genetic algorithm fuzzy c-means clustering |
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