  We consider a discrete latent variable model for two-way data arrays, which
allows one to simultaneously produce clusters along one of the data dimensions
(e.g. exchangeable observational units or features) and contiguous groups, or
segments, along the other (e.g. consecutively ordered times or locations). The
model relies on a hidden Markov structure but, given its complexity, cannot be
estimated by full maximum likelihood. We therefore introduce composite
likelihood methodology based on considering different subsets of the data. The
proposed approach is illustrated by simulation, and with an application to
genomic data.
