An improved hybrid genetic clustering algorithm

Authors:
Yongguo Liu;Jun Peng;Kefei Chen;Yi Zhang
Affiliations:
College of Computer Science and Engineering, University of Electronic, Science and Technology of China, Chengdu, P.R. China;School of Electronic Information Engineering, Chongqing University of Science and Technology, Chongqing, P.R. China;Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, P.R. China;College of Computer Science and Engineering, University of Electronic, Science and Technology of China, Chengdu, P.R. China
Venue:
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Year:
2006

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Abstract

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.