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Published in:
2006 | OriginalPaper | Chapter
An Improved Hybrid Genetic Clustering Algorithm
Authors : Yongguo Liu, Jun Peng, Kefei Chen, Yi Zhang
Published in: Advances in Artificial Intelligence
Publisher: Springer Berlin Heidelberg
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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.