文章基本信息

标题：CBGDC A new genetic center based data clustering algorithm based on K-means
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作者：Arash Ghorbannia Delavar ; Gholam Hasan Mohebpour
期刊名称：International Journal of Mechatronics, Electrical and Computer Technology
印刷版ISSN：2305-0543
出版年度：2014
卷号：4
期号：13
页码：1820-1839
出版社：Austrian E-Journals of Universal Scientific Organization
摘要：In this paper, a Center Based Genetic Data Clustering (CBGDC) algorithm based on K-means is proposed. This algorithm is able to detect arbitrary shape clusters and will not converge to local optima. In proposed algorithm a new population initialization method and reinsertion way have been used. Crossover and mutation operators will not be done with a fix probability and a new fitness function based on Silhouette index will be used to evaluate fitness of chromosomes faster. The efficiency of CBGDC has been compared with original genetic data clustering and K-means algorithm on artificial and real life datasets and experimental results show that the CBGDC will decrease clustering error more than original genetic data clustering and K-means
关键词：Data mining; data clustering; genetic algorithm; partitioning; K-means ; algorithm.