Cluster analysis is an important tool in a variety of scientific areas including pattern recognition, document clustering, and the analysis of microarray data. Although many clustering procedures such as hierarchical, strict partitioning and overlapping clusterings aim to construct an optimal partition of objects or, sometimes, variables, there are other methods, known as co-clustering or block clustering procedures, which consider the two sets simultaneously. In several situations, compared with the classical clustering algorithms, the co-clustering has been shown to be more effective in discovering hidden clustering structures in the data matrix. I will present different aims of co-clustering under several approaches. I will focus on block mixture models and the non-negative matrix factorization approach. Models, algorithms and applications will be presented.