Model-based co-clustering for mixed type data

Margot Selosse, Julien Jacques and Christophe Biernacki

Computational Statistics & Data Analysis, 2020, vol. 144, issue C

Abstract: 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.

Keywords: Co-clustering; Mixed-type data; Latent block model (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:144:y:2020:i:c:s016794731930221x

DOI: 10.1016/j.csda.2019.106866

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