TITLE

A review on particle swarm optimization algorithms and their applications to data clustering

AUTHOR(S)
Rana, Sandeep; Jasola, Sanjay; Kumar, Rajesh
PUB. DATE
March 2011
SOURCE
Artificial Intelligence Review;Mar2011, Vol. 35 Issue 3, p211
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Data clustering is one of the most popular techniques in data mining. It is a method of grouping data into clusters, in which each cluster must have data of great similarity and high dissimilarity with other cluster data. The most popular clustering algorithm K-mean and other classical algorithms suffer from disadvantages of initial centroid selection, local optima, low convergence rate problem etc. Particle Swarm Optimization (PSO) is a population based globalized search algorithm that mimics the capability (cognitive and social behavior) of swarms. PSO produces better results in complicated and multi-peak problems. This paper presents a literature survey on the PSO application in data clustering. PSO variants are also described in this paper. An attempt is made to provide a guide for the researchers who are working in the area of PSO and data clustering.
ACCESSION #
59258729

 

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