# Block clustering of Binary Data with Gaussian Co-variables

## TL;DR

This paper introduces a new co-clustering method for binary data that incorporates Gaussian co-variables, improving the grouping of rows and columns in large-scale data analysis.

## Contribution

It presents a novel latent block model that integrates co-variables into the co-clustering process, enhancing clustering accuracy.

## Key findings

- Effective on simulated datasets
- Successful application to genetic data
- Improved clustering performance

## 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.