A comparison between block CEM and two-way CEM algorithms to cluster a contingency table\

When the data consists of a set of objects described by a set of variables, we have recently proposed a new mixture model which takes into account the block clustering problem on the both sets and have developed the block CEM algorithm. In this paper, we embed the block clustering problem of contingency table in the mixture approach. In using a Poisson model and adopting the classification maximum likelihood principle we perform an adapted version of block CEM. We evaluate its performance and compare it to a simple use of CEM applied on the both sets separately. We present detailed experimental results on simulated data and we show the interest of this new algorithm.