Enhanced K-Means Clustering Algorithm Based on Particle Swarm Optimization

This MATLAB source code implements an enhanced K-means clustering algorithm that integrates particle swarm optimization (PSO) methodology. The algorithm employs PSO to optimize initial cluster centroids selection, overcoming traditional K-means' sensitivity to initialization. Key implementation features include dynamic inertia weight adjustment, global-best position tracking, and fitness function evaluation using intra-cluster distances. The code is designed for experimental validation across diverse datasets, with applications in image segmentation, data mining, and pattern recognition domains. The accompanying documentation details the algorithmic framework where PSO particles represent potential centroid solutions, evolving through velocity updates and position adjustments toward optimal clustering configurations. Comprehensive usage guidelines are provided to facilitate rapid implementation, including parameter configuration examples and dataset formatting specifications. Researchers interested in computational intelligence and clustering methodologies will find this implementation particularly valuable for comparative studies and practical applications. The code structure modularizes core components: PSO optimization engine, centroid initialization routines, and cluster validity evaluation metrics.