Hybridization of particle swarm optimization with the K-Means algorithm for clustering analysis

Clustering is an unsupervised classification technique which deals with pattern recognition problems. While traditional analytical methods suffer from slow convergence and the challenges of high-dimensional. Recent years, particle swarm optimization (PSO) has successfully been applied to a number of real world clustering problems with the fast convergence and the effectively for high-dimensional data. This paper presents a detailed overview of hybrid algorithms combining PSO with K-Means algorithm for solving clustering problem. For each algorithm, technical details that are required for applying clustering, such as its type, particle formulation, and the most efficient fitness functions are also discussed. Finally, a summary is given together with suggestions for future research.