Extended PSO Algorithm for Improvement Problems K-Means Clustering Algorithm
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.
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.
More Details: http://airccse.org/journal/ijmit/papers/6314ijmit02.pdf
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