DIVERSE DEPICTION OF PARTICLE SWARM OPTIMIZATION FOR DOCUMENT CLUSTERING

Abstract
Document clustering algorithms play an important task towards the goal of organizing huge amounts of documents into a small number of significant clusters. Traditional clustering algorithms will search only a small sub-set of possible clustering and as a result, there is no guarantee that the solution found will be optimal. This paper presents different representation of particle in Particle Swarm Optimization (PSO) for document clustering. Experiments results are examined with document corpus. It demonstrates that the Discrete PSO algorithm statistically outperforms the Binary PSO and Simple PSO for document Clustering.

Authors
K. Premalatha, A.M. Natarajan
Bannari Amman Institute of Technology, Tamil Nadu, India

Keywords
Particle Swarm Optimization, Document Clustering, Inertia Weight, Constriction Factor, Swarm Intelligence
Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 1 , Issue: 3 )
Date of Publication :
January 2011

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