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CLUSTERING USING AN IMPROVED HYBRID GENETIC ALGORITHM
刊名：
International Journal of Artificial Intelligence Tools
作者：
YONGGUO LIU
XIAORONG PU
YIDONG SHEN
ZHANG YI
XIAOFENG LIAO
刊号：
738GL060
ISSN：
0218-2130
出版年：
2007
年卷期：
2007, vol.16, no.6
页码：
919-934
总页数：
16
分类号：
TP18
关键词：
Clustering
；
Genetic algorithms
；
Local iteration algorithm
参考中译：
语种：
eng
文摘：
In this article, a new genetic clustering algorithm called the Improved Hybrid Genetic Clustering Algorithm (IHGCA) is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGCA, the improvement operation including five local iteration methods is developed to tune the individual and accelerate the convergence speed of the clustering algorithm, and the partition-absorption mutation operation is designed to reassign objects among different clusters. By experimental simulations, its superiority over some known genetic clustering methods is demonstrated.
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