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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 Meansdoi:10.1109/bicta.2010.5645181 dblp:conf/bic-ta/ShenJZZ10 fatcat:5a6pztu7vfdqrkec5apmizifii