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.
Document type :
Journal articles
Neurocomputing, Elsevier, 2013, 112, pp.164-171
Contributor : Julien Jacques <>
Submitted on : Saturday, October 13, 2012 - 10:52:50 AM
Last modification on : Tuesday, July 2, 2013 - 6:18:15 PM




  • HAL Id : hal-00628247, version 2



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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