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Abstract. Although many clustering procedures aim to construct an optimal partition of objects or, sometimes, of variables, there are other methods, called block clustering methods, which consider simultaneously the two sets and organize the data into homogeneous blocks. This kind of methods has practical importance in a wide of variety of applications such as text and market basket data analysis. Typically, the data that arises in these applications is arranged as two-way contingency table. Using Poisson distributions, a latent block model for these data is proposed and, setting it under the maximum likelihood approach and the classification maximum likelihood approach, various algorithms are proposed. Their performances are evaluated and compared to a simple use of EM or CEM applied separately on the rows and columns of the contingency table.

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International audienceWe present Coclus, a novel diagonal co-clustering algorithm which is able to effectively co-cluster binary or contingency matrices by directly maximizing an adapted version of the modularity measure traditionally used for networks. While some effective co-clustering algorithms already exist that use network-related measures (normalized cut, modularity), they do so by using spectral relaxations of the discrete optimization problems. In contrast, Coclus allows to get even better co-clusters by directly maximizing modularity using an iterative alternating optimization procedure. Extensive comparative experiments performed on various document-term datasets demonstrate that our algorithm is very effective, stable and outperforms other co-clustering algorithms

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