Multi-Partitions Subspace Clustering

Vandewalle, Vincent

Type de document :

Article dans une revue scientifique: Article original

DOI :

10.3390/math8040597

URL permanente :

http://hdl.handle.net/20.500.12210/90736

Titre :

Multi-Partitions Subspace Clustering

Auteur(s) :

Vandewalle, Vincent [Auteur]

MOdel for Data Analysis and Learning [MODAL]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Titre de la revue :

Mathematics

Nom court de la revue :

Mathematics

Numéro :

Date de publication :

2020-06-06

ISSN :

2227-7390

Mot(s)-clé(s) en anglais :

clustering
mixture model
factorial discriminant analysis
EM algorithm

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

In model based clustering, it is often supposed that only one clustering latent variable explains the heterogeneity of the whole dataset. However, in many cases several latent variables could explain the heterogeneity of ...
Lire la suite >In model based clustering, it is often supposed that only one clustering latent variable explains the heterogeneity of the whole dataset. However, in many cases several latent variables could explain the heterogeneity of the data at hand. Finding such class variables could result in a richer interpretation of the data. In the continuous data setting, a multi-partition model based clustering is proposed. It assumes the existence of several latent clustering variables, each one explaining the heterogeneity of the data with respect to some clustering subspace. It allows to simultaneously find the multi-partitions and the related subspaces. Parameters of the model are estimated through an EM algorithm relying on a probabilistic reinterpretation of the factorial discriminant analysis. A model choice strategy relying on the BIC criterion is proposed to select to number of subspaces and the number of clusters by subspace. The obtained results are thus several projections of the data, each one conveying its own clustering of the data. Model’s behavior is illustrated on simulated and real data.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CHU Lille

Collections :

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

Date de dépôt :

2023-11-15T09:42:31Z
2023-12-13T13:55:00Z

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mathematics-08-00597-v2.pdf
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Ce document est diffusé sous licence Attribution 3.0 United States

Numéro de version	Lien	Date de modification
2	20.500.12210/90736*	2023-12-13T13:52:34Z
1	20.500.12210/90736.1	2023-11-15T09:42:31Z

Multi-Partitions Subspace Clustering

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Multi-Partitions Subspace Clustering

BibTeX

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Excel

RIS