Title :

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

Author(s) :

Jacques, Julien [Auteur]
Preda, Cristian [Auteur]

Journal title :

Neurocomputing

Volume number :

112

Pages :

164-171

Publisher :

Elsevier

Publication date :

2013

ISSN :

0925-2312

Keyword(s) :

Functional data
Model-based clustering
Random variable density
Functional principal component analysis

HAL domain(s) :

Mathématiques [math]/Statistiques [math.ST]

English abstract : [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. ...
Show more >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.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

CNRS
Université de Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Submission date :

2020-06-08T14:11:28Z
2020-06-09T09:24:17Z