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

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.

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An, L.T.H., Belghiti, M.T. & Tao, P.D. A new efficient algorithm based on DC programming and DCA for clustering. J Glob Optim 37, 593–608 (2007). https://doi.org/10.1007/s10898-006-9066-4

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Keywords

  • Clustering
  • K-median problem
  • K-means algorithm
  • DC programming
  • DCA
  • Nonsmooth nonconvex programming

AMS subject classifications

  • 65K05
  • 65K10
  • 90C26
  • 90C90
  • 15A60
  • 90C06