Title :

Latent class model with conditional dependency per modes to cluster categorical data

Author(s) :

Marbac, Matthieu [Auteur correspondant]
MOdel for Data Analysis and Learning [MODAL]
Biernacki, Christophe [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Vandewalle, Vincent [Auteur]
MOdel for Data Analysis and Learning [MODAL]

Journal title :

Advances in Data Analysis and Classification

Pages :

183–207

Publisher :

Springer Verlag

Publication date :

2016-06-01

ISSN :

1862-5347

English keyword(s) :

model selection.
mixture models
integrated complete-data likelihood
Metropolis-within-Gibbs sampler
model selection
categorical data
clustering

HAL domain(s) :

Statistiques [stat]/Méthodologie [stat.ME]

English abstract : [en]

We propose a parsimonious extension of the classical latent class model to cluster categorical data by relaxing the class conditional independence assumption. Under this new mixture model, named Conditional Modes Model, ...
Show more >We propose a parsimonious extension of the classical latent class model to cluster categorical data by relaxing the class conditional independence assumption. Under this new mixture model, named Conditional Modes Model, variables are grouped into conditionally independent blocks. The corresponding block distribution is a parsimonious multinomial distribution where the few free parameters correspond to the most likely modality crossings, while the remaining probability mass is uniformly spread over the other modality crossings. Thus, the proposed model allows to bring out the intra-class dependency between variables and to summarize each class by a few characteristic modality crossings. The model selection is performed via a Metropolis-within-Gibbs sampler to overcome the computational intractability of the block structure search. As this approach involves the computation of the integrated complete-data likelihood, we propose a new method (exact for the continuous parameters and approximated for the discrete ones) which avoids the biases of the \textsc{bic} criterion pointed out by our experiments. Finally, the parameters are only estimated for the best model via a MCMC algorithm. The characteristics of the new model are illustrated on simulated data and on two biological data sets. These results strengthen the idea that this simple model allows to reduce biases involved by the conditional independence assumption and gives meaningful parameters. Both applications were performed with the R package CoModesShow less >

Language :

Anglais

Popular science :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL