Clustering with noising method

Authors:
Yongguo Liu;Yan Liu;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
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

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Abstract

The minimum sum of squares clustering problem is a nonconvex program which possesses many locally optimal values, resulting that its solution often falls into these traps. In this article, a recent metaheuristic technique, the noising method, is introduced to explore the proper clustering of data sets under the criterion of minimum sum of squares clustering. Meanwhile, K-means algorithm as a local improvement operation is integrated into the noising method to improve the performance of the clustering algorithm. Extensive computer simulations show that the proposed approach is feasible and effective.