Co-clustering algorithms can seek homogeneous sub-matrices into a dyadic data matrix, such as a document-word matrix.

We unified some latent block models by proposing a flexible ELBM that is extended to SELBM to address the sparse problem by revealing a diagonal structure from sparse datasets. This leads to obtain more homogeneous co-clusters and therefore produce useful, ready-to-use and easy-to-interpret results.