Hierarchical clustering based on mathematical optimization

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, France;Laboratory of Theoretical and Applied Computer Science – LITA EA 3097, UFR MIM, University of Paul Verlaine, Metz, France;Laboratory of Modelling, Optimization & Operations Research, National Institute for Applied Sciences – Rouen, Mont Saint Aignan, France
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

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Abstract

In this paper a novel optimization model for bilevel hierarchical clustering has been proposed. This is a hard nonconvex, nonsmooth optimization problem for which we investigate an efficient technique based on DC (Difference of Convex functions) programming and DCA (DC optimization Algorithm). Preliminary numerical results on some artificial and real-world databases show the efficiency and the superiority of this approach with respect to related existing methods.