Authors: Michael P.B. Gallaugher, Christophe Biernacki, and Paul D. McNicholas

A co-clustering model for continuous data that relaxes the identically distributed assumption within blocks of traditional co-clustering will be presented. The proposed model, although allowing more flexibility, still maintains the very high degree of parsimony achieved by traditional co-clustering. A stochastic EM algorithm along with a Gibbs sampler is used for parameter estimation and an ICL criterion is used for model selection. Simulated and real datasets will be used for illustration and comparison with traditional co-clustering.