  In recent years, data dimensionality has increasingly become a concern,
leading to many parameter and dimension reduction techniques being proposed in
the literature. A parameter-wise co-clustering model, for data modelled via
continuous random variables, is presented. The proposed model, although
allowing more flexibility, still maintains the very high degree of parsimony
achieved by traditional co-clustering. A stochastic expectation-maximization
(SEM) algorithm along with a Gibbs sampler is used for parameter estimation and
an integrated complete log-likelihood criterion is used for model selection.
Simulated and real datasets are used for illustration and comparison with
traditional co-clustering.
