Minimization subproblems and heuristics for an applied clustering problem

Minimization subproblems and heuristics for an applied clustering problem

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Article ID: iaor20042896
Country: Netherlands
Volume: 146
Issue: 1
Start Page Number: 19
End Page Number: 34
Publication Date: Jan 2003
Journal: European Journal of Operational Research
Authors: , ,
Keywords: heuristics, programming: nonlinear
Abstract:

A practical problem that requires the classification of a set of points of ℝn using a criterion not sensitive to bounded outliers is studied in this paper. A fixed-point (k-means) algorithm is defined that uses an arbitrary distance function. Finite convergence is proved. A robust distance defined by Boente et al. is selected for applications. Smooth approximations of this distance are defined and suitable heuristics are introduced to enhance the probability of finding global optimizers. A real-life example is presented and commented.

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