Block clustering of Binary Data with Gaussian Co-variables

2018/12/20 by Serge Iovleff, Iovleff, Serge, Syllla, Seydou +2
Biochemistry, Genetics and Molecular Biology · Computer Science · #Applications (stat.AP) #Bayesian Methods and Mixture Models #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #FOS: Mathematics #Gene expression and cancer classification #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1812.08520

openalex publication_date 2018/12/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The simultaneous grouping of rows and columns is an important technique that is increasingly used in large-scale data analysis. In this paper, we present a novel co-clustering method using co-variables in its construction. It is based on a latent block model taking into account the problem of grouping variables and clustering individuals by integrating information given by sets of co-variables. Numerical experiments on simulated data sets and an application on real genetic data highlight the interest of this approach.

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