期刊名称：International Journal of Managing Information Technology
印刷版ISSN：0975-5926
电子版ISSN：0975-5586
出版年度：2014
卷号：6
期号：3
DOI：10.5121/ijmit.2014.6302
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The clustering is a without monitoring process and one of the most common data mining techniques. Thepurpose of clustering is grouping similar data together in a group, so were most similar to each other in acluster and the difference with most other instances in the cluster are. In this paper we focus on clusteringpartition k-means, due to ease of implementation and high-speed performance of large data sets, After 30year it is still very popular among the developed clustering algorithm and then for improvement problem ofplacing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.Our new algorithm is able to be cause of exit from local optimal and with high percent produce theproblem’s optimal answer. The probe of results show that mooted algorithm have better performanceregards as other clustering algorithms specially in two index, the carefulness of clustering and the qualityof clustering.
关键词：Clustering; Data Mining; Extended chaotic particle swarm optimization; K-means algorithm
