DIVERSE DEPICTION OF PARTICLE SWARM OPTIMIZATION FOR DOCUMENT CLUSTERING

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.