Model-based approach to biclustering ordinal data

A finite mixture model to simultaneously cluster the rows and columns of two-mode ordinal data matrix is proposed. Due to the numerical intractability of the likelihood function, estimation of model parameters is based on composite likelihood (CL) methods and essentially reduces to a computationally efficient Expectation-Maximization type algorithm. The performance of the proposed approach is discussed on both simulated and real datasets. The results are encouraging and would deserve further discussion