In this paper, a new genetic clustering algorithm called IHGA-clustering is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGA-clustering, DHB operation is developed to improve the individual and accelerate the convergence speed, and partition-mergence mutation operation is designed to reassign objects among different clusters. Equipped with these two components, IHGA-clustering can stably output the proper result. Its superiority over HGA-clustering, GKA, and KGA-clustering is extensively demonstrated for experimental data sets. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Liu, Y., Peng, J., Chen, K., & Zhang, Y. (2006). An improved hybrid genetic clustering algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3955 LNAI, pp. 192–202). Springer Verlag. https://doi.org/10.1007/11752912_21
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