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A new efficient algorithm based on DC programming and DCA for clustering. (English) Zbl 1198.90327
Summary: 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.

MSC:
90C26 Nonconvex programming, global optimization
65K10 Numerical optimization and variational techniques
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