%0 Journal Article
%T Model-based co-clustering for mixed type data
%+ Entrepôts, Représentation et Ingénierie des Connaissances (ERIC)
%+ Université Lumière - Lyon 2 (UL2)
%+ MOdel for Data Analysis and Learning (MODAL)
%A Selosse, Margot
%A Jacques, Julien
%A Biernacki, Christophe
%< avec comité de lecture
%@ 0167-9473
%J Computational Statistics and Data Analysis
%I Elsevier
%V 144
%P 106866
%8 2020
%D 2020
%R 10.1016/j.csda.2019.106866
%K co-clustering
%K mixed-type data
%K latent block model
%K latent block model * Corresponding author
%Z Mathematics [math]/Statistics [math.ST]Journal articles
%X The importance of clustering for creating groups of observations is well known. The emergence of high-dimensional data sets with a huge number of features leads to co-clustering techniques, and several methods have been developed for simultaneously producing groups of observations and features.By grouping the data set into blocks (the crossing of a row-cluster and a column-cluster), these techniques can sometimes better summarize the data set and its inherent structure. The Latent Block Model (LBM) is a well-known method for performing co-clustering. However, recently, contexts with features of different types (here called mixed type data sets) are becoming more common. The LBM is not directly applicable to this kind of data set. Here a natural extension of the usual LBM to the ``Multiple Latent Block Model" (MLBM) is proposed in order to handle mixed type data sets. Inference is performed using a Stochastic EM-algorithm that embeds a Gibbs sampler, and allows for missing data situations. A model selection criterion is defined to choose the number of row and column clusters. The method is then applied to both simulated and real data sets.
%G English
%2 https://hal.science/hal-01893457v2/document
%2 https://hal.science/hal-01893457v2/file/manuscript.pdf
%L hal-01893457
%U https://hal.science/hal-01893457
%~ CNRS
%~ INRIA
%~ UNIV-LYON1
%~ UNIV-LYON2
%~ INRIA-LILLE
%~ ERIC
%~ INRIA_TEST
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%~ TESTALAIN1
%~ INRIA2
%~ UNIV-LILLE
%~ LYON2
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%~ UNIV-LYON
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%~ LPP-MATH
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