A hybrid tabu search based clustering algorithm

Authors:
Yongguo Liu;Yan Liu;Libin Wang;Kefei Chen
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, P.R. China;School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu, P.R. China;Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, P.R. China;Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, P.R. China
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2005

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Abstract

The clustering problem under the criterion of minimum sum of squares clustering is a nonconvex program which possesses many locally optimal values, resulting that its solution often falls into these traps. In this paper, a hybrid tabu search based clustering algorithm called KT-Clustering is developed to explore the proper clustering of data sets. Based on the framework of tabu search, KT-Clustering gathers the optimization property of tabu search and the local search capability of K-means algorithm together. Moreover, mutation operation is adopted to establish the neighborhood of KT-Clustering. Its superiority over K-means algorithm, a genetic clustering algorithm and another tabu search based clustering algorithm is extensively demonstrated for experimental data sets.