EXTENDED PSO ALGORITHM FOR IMPROVEMENT PROBLEMS K-MEANS CLUSTERING ALGORITHM
International Journal of Managing Information Technology (IJMIT)
ISSN: 0975-5586 (Online); 0975-5926 (Print)
Article:
EXTENDED PSO ALGORITHM FOR IMPROVEMENT PROBLEMS K-MEANS CLUSTERING ALGORITHM
Authors
Maryam Lashkari1
and Amin Rostami2
1Department of Computer Engineering, Ferdows Branch, Islamic Azad University, Ferdows, Iran.
2Department of Computer Engineering, Ferdows Branch, Islamic Azad University, Ferdows, Iran.
1Department of Computer Engineering, Ferdows Branch, Islamic Azad University, Ferdows, Iran.
2Department of Computer Engineering, Ferdows Branch, Islamic Azad University, Ferdows, Iran.
Abstract
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.
Keywords
Clustering, Data Mining, Extended chaotic particle swarm optimization, K-means algorithm.
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