| The Effective Clustering Partition Algorithm Based on the Genetic Evolution |
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| 作者姓名： | 廖芹 李希雯 |
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| 作者单位： | School of Mathematical Sciences, South China University of Technology, Guangzhou 510640 |
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| 摘 要： | To the problem that it is hard to determine the clustering number and the abnormal points by using the clustering validity function, an effective clustering partition model based on the genetic algorithm is built in this paper. The solution to the problem is formed by the combination of the clustering partition and the encoding samples, and the fitness function is defined by the distances among and within clusters. The clustering number and the samples in each cluster are determined and the abnormal points are distinguished by implementing the triple random crossover operator and the mutation. Based on the known sample data, the results of the novel method and the clustering validity function are compared. Numerical experiments are given and the results show that the novel method is more effective.
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| 关 键 词： | 遗传进化 计算方法 聚类 解码技术 |
| 收稿时间： | 2006-08-20 |
The Effective Clustering Partition Algorithm Based on the Genetic Evolution |
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| LIAO Qin,LI Xi-wen.The Effective Clustering Partition Algorithm Based on the Genetic Evolution[J].Journal of Donghua University,2006,23(6):43-46. |
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| Authors: | LIAO Qin LI Xi-wen |
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| Institution: | School of Mathematical Sciences, South China University of Technology, Guangzhou 510640 |
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| Abstract: | To the problem that it is hard to determine the clustering number and the abnormal points by using the clustering validity function, an effective clustering partition model based on the genetic algorithm is built in this paper. The solution to the problem is formed by the combination of the clustering partition and the encoding samples, and the fitness function is defined by the distances among and within clusters. The clustering number and the samples in each cluster are determined and the abnormal points are distinguished by implementing the triple random crossover operator and the mutation. Based on the known sample data, the results of the novel method and the clustering validity function are compared. Numerical experiments are given and the results show that the novel method is more effective. |
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| Keywords: | clustering validity genetic algorithm clustering number abnormal point |
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