Artificial Intelligence Review

, Volume 35, Issue 3, pp 211–222

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



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.


Data mining Data clustering K-mean clustering Particle swarm optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.School of ICTGautam Buddha UniversityGreater NoidaIndia
  2. 2.Department of Electrical EngineeringMalaviya National Institute of TechnologyJaipurIndia

Personalised recommendations