An approach for document clustering using PSO and K-means algorithm

The world wide web also known as WWW is the source for the largest shared information. To effectively organize, summarize and navigate through the information on the web in a fast and high quality manner, document clustering algorithms are needed. Various clustering algorithms are proposed by the researchers in which the K-means is widely used partitioning clustering algorithm which is easy for implementation, has fast convergence property in local area, and takes less time for execution. But major drawback of this method is its random choice of initial cluster centroids. To overcome this problem, an approach for document clustering using Particle Swarm Optimization (PSO) method is proposed in this paper. PSO method is applied before K-means for finding the optimal points in the search space and these points are used as initial cluster centroids for K-means algorithm to find final clusters of documents. Results of clustering algorithms are tested on four different document datasets. The outcome shows that the most efficient clustering results are generated than traditional K-means algorithm.