  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 we need to account for relations among both time
instants and variables and, at the same time, for subjects heterogeneity. We
propose a new co-clustering methodology for clustering individuals and
variables simultaneously that is designed to handle both functional and
longitudinal data. Our approach borrows some concepts from the curve
registration framework by embedding the Shape Invariant Model in the Latent
Block Model, estimated via a suitable modification of the SEM-Gibbs algorithm.
The resulting procedure allows for several user-defined specifications of the
notion of cluster that could be chosen on substantive grounds and provides
parsimonious summaries of complex longitudinal or functional data by
partitioning data matrices into homogeneous blocks.
