A new efficient algorithm based on DC programming and DCA for clustering

Authors:
Le Thi An;M. Tayeb Belghiti;Pham Dinh Tao
Affiliations:
Laboratory of Theoretical and Applied Computer Science, UFR MIM, University of Paul, Metz, France 57045;Laboratory of Modelling, Optimization and Operations Research, National Institute for Applied Sciences - Rouen, Mont Saint Aignan Cedex, France 76131;Laboratory of Modelling, Optimization and Operations Research, National Institute for Applied Sciences - Rouen, Mont Saint Aignan Cedex, France 76131
Venue:
Journal of Global Optimization
Year:
2007

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Abstract

In this paper, a version of K-median problem, one of the most popular and best studied clustering measures, is discussed. The model using squared Euclidean distances terms to which the K-means algorithm has been successfully applied is considered. A fast and robust algorithm based on DC (Difference of Convex functions) programming and DC Algorithms (DCA) is investigated. Preliminary numerical solutions on real-world databases show the efficiency and the superiority of the appropriate DCA with respect to the standard K-means algorithm.