A Comparative Analysis of Unsupervised K-Means, PSO and Self-Organizing PSO for Image Clustering

This paper presents a comparative analysis of three algorithms namely K-means, Particle swarm Optimization (PSO) and Self-Organizing PSO (SOPSO) for image clustering problems. The traditional K-means algorithm found to be trapped in local minima. However, PSO and SOPSO overcome the problem of local minima and provide better results. In this work gbest model is used in PSO and both West and gbest models are used in SOPSO based on self-Organizing rules. It is shown that PSO and SOPSO produce better results compared to K-means with respect to the quantization error, inter- and intra-cluster distances.