Model-Based Co-clustering for Continuous Data
Résumé
The co-clustering consists in reorganizing a data matrix into homogeneous blocks by considering simultaneouslythe sets of rows and columns. Setting this aim in model-based clustering, adapted block latent models were proposed for binary data and co-occurrence matrix. Regarding continuous data, the latent block model is not appropriated in many cases. As nonnegative matrix factorization, it treats symmetrically the two sets, and the estimation of associated parameters requires a variational approximation. In this paper we focus on continuous data matrix without restriction to non negative matrix. We propose a parsimonious mixture model allowing to overcome the limits of the latent block model.