Gaussian kernel minimum sum-of-squares clustering and solution method based on DCA

Authors:
Le Hoai Minh;Le Thi Hoai An;Pham Dinh Tao
Affiliations:
Laboratory of Theoretical and Applied Computer Science - LITA EA 3097, UFR MIM, University of Paul Verlaine - Metz, Metz, France;Laboratory of Theoretical and Applied Computer Science - LITA EA 3097, UFR MIM, University of Paul Verlaine - Metz, Metz, France;Laboratory of Modelling, Optimization & Operations Research, National Institute for Applied Sciences - Rouen, Saint-Etienne-du-Rouvray Cedex, France
Venue:
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2012

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Abstract

In this paper, a Gaussian Kernel version of the Minimum Sum-of-Squares Clustering GKMSSC) is studied. The problem is formulated as a DC (Difference of Convex functions) program for which a new algorithm based on DC programming and DCA (DC Algorithm) is developed. The related DCA is original and very inexpensive. Numerical simulations show the efficiency of DCA and its superiority with respect to K-mean, a standard method for clustering.