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Journal Articles Advances in Data Analysis and Classification Year : 2016

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

Abstract

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 CoModes
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Dates and versions

hal-00950112 , version 1 (20-02-2014)
hal-00950112 , version 2 (05-01-2017)

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Matthieu Marbac, Christophe Biernacki, Vincent Vandewalle. Latent class model with conditional dependency per modes to cluster categorical data. Advances in Data Analysis and Classification, 2016, 10 (2), pp.183-207. ⟨10.1007/s11634-016-0250-1⟩. ⟨hal-00950112v2⟩
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