A novel clustering technique based on improved noising method

Authors:
Yongguo Liu;Wei Zhang;Dong Zheng;Kefei Chen
Affiliations:
College of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P. R. China;Department of Computer and Modern Education Technology, Chongqing Education College, Chongqing, 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:
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2005

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Abstract

In this article, the clustering problem under the criterion of minimum sum of squares clustering is considered. It is known that this problem is a nonconvex program which possesses many locally optimal values, resulting that its solution often falls into these traps. To explore the proper result, a novel clustering technique based on improved noising method called INMC is developed, in which one-step DHB algorithm as the local improvement operation is integrated into the algorithm framework to fine-tune the clustering solution obtained in the process of iterations. Moreover, a new method for creating the neighboring solution of the noising method called mergence and partition operation is designed and analyzed in detail. Compared with two noising method based clustering algorithms recently reported, the proposed algorithm greatly improves the performance without the increase of the time complexity, which is extensively demonstrated for experimental data sets.