# Un mod\`ele Bay\'esien de co-clustering de donn\'ees mixtes

## TL;DR

This paper introduces a parameter-free Bayesian co-clustering method for mixed-type data tables, optimizing model quality through a Bayesian criterion and continuous optimization, demonstrated on real datasets.

## Contribution

It presents a novel MAP Bayesian approach for co-clustering mixed data types that is parameter-free and uses an exact Bayesian criterion for model evaluation.

## Key findings

- Effective co-clustering of mixed data types demonstrated on real datasets
- Parameter-free approach simplifies user interaction and model selection
- Bayesian criterion provides an exact measure of model quality

## Abstract

We propose a MAP Bayesian approach to perform and evaluate a co-clustering of mixed-type data tables. The proposed model infers an optimal segmentation of all variables then performs a co-clustering by minimizing a Bayesian model selection cost function. One advantage of this approach is that it is user parameter-free. Another main advantage is the proposed criterion which gives an exact measure of the model quality, measured by probability of fitting it to the data. Continuous optimization of this criterion ensures finding better and better models while avoiding data over-fitting. The experiments conducted on real data show the interest of this co-clustering approach in exploratory data analysis of large data sets.