Funclust: a curves clustering method using functional random variables density approximation

Julien Jacques 1, 2 Cristian Preda 1, 2
2 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, Laboratoire de Mathématiques Paul Painlevé, Santé Publique : Épidémiologie et Qualité des soins
Abstract : A new method for clustering functional data is proposed under the name Funclust. This method relies on the approximation of the notion of probability density for functional random variables, which generally does not exists. Using the Karhunen-Loeve expansion of a stochastic process, this approximation leads to define an approximation for the density of functional variables. Based on this density approximation, a parametric mixture model is proposed. The parameter estimation is carried out by an EM-like algorithm, and the maximum a posteriori rule provides the clusters. The efficiency of Funclust is illustrated on several real datasets, as well as for the characterization of the Mars surface.
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Article dans une revue
Neurocomputing, Elsevier, 2013, 112, pp.164-171


https://hal.archives-ouvertes.fr/hal-00628247
Contributeur : Julien Jacques <>
Soumis le : samedi 13 octobre 2012 - 10:52:50
Dernière modification le : mardi 2 juillet 2013 - 18:18:15

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Julien Jacques, Cristian Preda. Funclust: a curves clustering method using functional random variables density approximation. Neurocomputing, Elsevier, 2013, 112, pp.164-171. <hal-00628247v2>

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