Simultaneous dimension reduction and ...
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès sans actes
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Title :
Simultaneous dimension reduction and multi-objective clustering using probabilistic factorial discriminant analysis
Author(s) :
Vandewalle, Vincent [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]

METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Conference title :
CMStatistics 2016
City :
Sevilla
Country :
Espagne
Start date of the conference :
2016-12-09
HAL domain(s) :
Statistiques [stat]
English abstract : [en]
In model based clustering of quantitative data it is often supposed that only one clustering variable explains the heterogeneity of all the others variables. However, when variables come from different sources, it is often ...
Show more >In model based clustering of quantitative data it is often supposed that only one clustering variable explains the heterogeneity of all the others variables. However, when variables come from different sources, it is often unrealistic to suppose that the heterogeneity of the data can only be explained by one variable. If such an assumption is made, this could lead to a high number of clusters which could be difficult to interpret. A model based multi-objective clustering is proposed, is assumes the existence of several latent clustering variables, each one explaining the heterogeneity of the data on some clustering projection. In order to estimate the parameters of the model an EM algorithm is proposed, it mainly relies on a reinterpretation of the standard factorial discriminant analysis in a probabilistic way. The obtained results are projections of the data on some principal clustering components allowing some synthetic interpretation of the principal clusters raised by the data. The behavior of the model is illustrated on simulated and real data.Show less >
Show more >In model based clustering of quantitative data it is often supposed that only one clustering variable explains the heterogeneity of all the others variables. However, when variables come from different sources, it is often unrealistic to suppose that the heterogeneity of the data can only be explained by one variable. If such an assumption is made, this could lead to a high number of clusters which could be difficult to interpret. A model based multi-objective clustering is proposed, is assumes the existence of several latent clustering variables, each one explaining the heterogeneity of the data on some clustering projection. In order to estimate the parameters of the model an EM algorithm is proposed, it mainly relies on a reinterpretation of the standard factorial discriminant analysis in a probabilistic way. The obtained results are projections of the data on some principal clustering components allowing some synthetic interpretation of the principal clusters raised by the data. The behavior of the model is illustrated on simulated and real data.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
CHU Lille
Université de Lille
Université de Lille
Submission date :
2020-06-08T14:10:19Z
2020-06-09T08:29:15Z
2020-06-09T08:29:15Z
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