Clustering analysis by Improved Particle Swarm Optimization and K-means algorithm

This paper presents an Improved Particle Swarm Optimization (IPSO) and K-means algorithm for solving clustering problems for document and avoid trapping in a local optimal solution. Recent studies have shown that partitional clustering algorithms are more suitable for clustering large datasets. The K-means algorithm is the most commonly used partitional clustering algorithm because it can be easily implemented and is the most efficient one in terms of the execution time. The major problem with this algorithm is that it is sensitive to the selection of the initial partition and may converge to local optima. So here used Improved Particle Swarm Optimization (IPSO) +K-means document clustering algorithm. The proposed solution generates more accurate, robust and better clustering results when compared with K-means and PSO. IPSO algorithm is applied for four different text document datasets. The number of documents in the datasets range from 204 to over 800, and the number of terms range from over 5000 to over 7000 are take for analysis.