Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …

This chapter presents some of the Bayesian solutions to the different interpretations of
picking the “right” number of components in a mixture, before concluding on the ill-posed …

Modelling relationships between individuals is a classical question in social sciences and
clustering individuals according to the observed patterns of interactions allows us to uncover …

We present here model-based co-clustering methods, with a focus on the latent block model
(LBM). We introduce several specifications of the LBM (standard, sparse, Bayesian) and …

In this paper, we present a novel method for co-clustering, an unsupervised learning
approach that aims at discovering homogeneous groups of data instances and features by …

The integrated completed likelihood (ICL) criterion has proven to be a very popular
approach in model-based clustering through automatically choosing the number of clusters …

We consider the simultaneous clustering of rows and columns of a matrix and more
particularly the ability to measure the agreement between two co-clustering partitions. The …

We study bipartite community detection in networks, or more generally the network
biclustering problem. We present a fast two-stage procedure based on spectral initialization …

In cluster analysis interest lies in probabilistically capturing partitions of individuals, items or
observations into groups, such that those belonging to the same group share similar …

Multivariate time-dependent data, where multiple features are observed over time for a set of
individuals, are increasingly widespread in many application domains. To model these data …