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- Publisher
- Springer Journals
- Copyright
- Copyright © 2010 by Springer Science+Business Media B.V.
- Subject
- Computer Science; Artificial Intelligence (incl. Robotics); Computer Science, general
- ISSN
- 0269-2821
- eISSN
- 1573-7462
- DOI
- 10.1007/s10462-010-9191-9
- Publisher site
- See Article on Publisher Site
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.
Journal
Artificial Intelligence Review – Springer Journals
Published: Nov 24, 2010