Titre :

Gaussian Based Visualization of Gaussian and Non-Gaussian Based Clustering

Auteur(s) :

Biernacki, Christophe [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Marbac, Matthieu [Auteur]
Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] [ENSAI]
Vandewalle, Vincent [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
MOdel for Data Analysis and Learning [MODAL]

Titre de la revue :

Journal of Classification

Éditeur :

Springer Verlag

Date de publication :

2020-07-11

ISSN :

0176-4268

Mot(s)-clé(s) en anglais :

factorial analysis
linear discriminant analysis
Gaussian mixture
model-based clustering
visualization
Dimension reduction

Discipline(s) HAL :

Statistiques [stat]/Méthodologie [stat.ME]

Résumé en anglais : [en]

A generic method is introduced to visualize in a "Gaussian-like way", and onto R 2 , results of Gaussian or non-Gaussian based clustering. The key point is to explicitly force a visualization based on a spherical Gaussian ...
Lire la suite >A generic method is introduced to visualize in a "Gaussian-like way", and onto R 2 , results of Gaussian or non-Gaussian based clustering. The key point is to explicitly force a visualization based on a spherical Gaussian mixture to inherit from the within cluster overlap that is present in the initial clustering mixture. The result is a particularly user-friendly drawing of the clusters, providing any practitioner with an overview of the potentially complex clustering result. An entropic measure provides information about the quality of the drawn overlap compared to the true one in the initial space. The proposed method is illustrated on four real data sets of different types (categorical, mixed, functional and network) and is implemented on the R package ClusVis.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

Modélisation et Inférence Statistique pour l'Apprentissage non-supervisé à partir de Données Massives

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL