Titre :

Parameter Setting for Evolutionary Latent Class Clustering

Auteur(s) :

Tessier, Damien [Auteur]
INRIA Futurs
Machine Learning and Optimisation [TAO]
Schoenauer, Marc [Auteur]
INRIA Futurs
Machine Learning and Optimisation [TAO]
Biernacki, Christophe [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Celeux, Gilles [Auteur]
Model selection in statistical learning [SELECT]
Govaert, Gérard [Auteur]
Heuristique et Diagnostic des Systèmes Complexes [Compiègne] [Heudiasyc]

Éditeur(s) ou directeur(s) scientifique(s) :

Lishan Kang and Yong Liu and Sanyou Y. Zeng

Titre de la manifestation scientifique :

Second International Symposium, ISICA 2007

Ville :

Wuhan

Pays :

Chine

Date de début de la manifestation scientifique :

2007-09-21

Titre de la revue :

LNCS

Éditeur :

Springer Verlag

Date de publication :

2007

Discipline(s) HAL :

Informatique [cs]/Intelligence artificielle [cs.AI]
Mathématiques [math]/Statistiques [math.ST]

Résumé en anglais : [en]

The latent class model or multivariate multinomial mixture is a powerful model for clustering discrete data. This model is expected to be useful to represent non-homogeneous populations. It uses a conditional independence ...
Lire la suite >The latent class model or multivariate multinomial mixture is a powerful model for clustering discrete data. This model is expected to be useful to represent non-homogeneous populations. It uses a conditional independence assumption given the latent class to which a statistical unit is belonging. However, it leads to a criterion that proves difficult to optimise by the standard approach based on the EM algorithm. An Evolutionary Algorithms is designed to tackle this discrete optimisation problem, and an extensive parameter study on a large artificial dataset allows to derive stable parameters. Those parameters are then validated on other artificial datasets, as well as on some well-known real data: the Evolutionary Algorithm performs repeatedly better than other standard clustering techniques on the same data.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL