Titre :

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

Auteur(s) :

Jacques, Julien [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Preda, Cristian [Auteur]
MOdel for Data Analysis and Learning [MODAL]

Titre de la revue :

Neurocomputing

Pagination :

164-171

Éditeur :

Elsevier

Date de publication :

2013-07-18

ISSN :

0925-2312

Mot(s)-clé(s) en anglais :

Functional data
model-based clustering
random variable density
functional principal component analysis

Discipline(s) HAL :

Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]

Résumé en anglais : [en]

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. ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL