EXTENDED PSO ALGORITHM FOR IMPROVEMENT PROBLEMS K-MEANS CLUSTERING ALGORITHM

The clustering is a without monitoring process and one of the most common data mining techniques. The purpose of clustering is grouping similar data together in a group, so were most similar to each other in a cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30 year it is still very popular among the developed clustering algorithm and then for improvement problem of placing 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 the problem’s optimal answer. The probe of results show that mooted algorithm have better performance regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality of clustering.