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Clustering is an active research topic in data mining and different methods have been proposed in the literature. Most of these methods are based on the use of a distance measure defined either on numerical attributes or on categorical attributes. However, in fields such as road traffic and medicine, datasets are composed of numerical and categorical(More)
Claudia Bauzer-Medeiros∗, Olivier Carles∗∗, Florian Devuyst∗∗∗, Georges Hébrail∗∗∗∗, Bernard Hugueney, Marc Joliveau∗∗∗, Geneviève Jomier, Maude Manouvrier, Yosr Naïja, Gérard Scemama∗∗, Laurent Steffan ∗Institute of Computing (IC) University of Campinas Caixa Postal 6176 13084-971 Campinas, SP Brazil cmbm@ic. unicamp. br ∗∗INRETS Laboratoire GRETIA 2,(More)
Validation and interpretation are the two last steps of a clustering process. Generally these steps are processed separately since the existing validity measures are not intended to express the interpretability or the non interpretability of clusters. We propose in this paper to merge the validation and interpretation steps by using a new supervised measure(More)
The clustering validation and clustering interpretation are the two last steps of clustering process. The validation step permits to evaluate the goodness of clustering results using some measures. Valid results are then generally interpreted and used in cluster analysis. The validity measures are classified into three categories: unsupervised measures,(More)
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