Clustering analysis aims at discovering groups and identifying interesting distributions and patterns in data sets. It can help the user to distinguish the structure of data and simplify the complexity of data from mass information. A particle swarm optimization-based clustering technique that utilized the principles of K-means algorithm, called KPSO-clustering, is proposed in this article. We attempt to integrate the effectiveness of the K-means algorithm for partitioning data into a number of clusters, with the capability of PSO to bring it out of the local minima. Finally, the effectiveness of the KPSO-clustering is demonstrated on four artificial data sets.
Proceedings of 2003 International Conference on Informations, Cybernetics, and Systems，頁1470-1475