Co-clustering aims to identify block patterns in a data table, from a joint clustering of rows and columns. This problem has been studied since 1965, with recent interests in various fields, ranging from graph analysis, machine learning, data mining and genomics. Several variants have been proposed with diverse names: bi-clustering, block clustering, cross-clustering, or simultaneous clustering. We propose here a review of these methods in order to describe, compare and discuss the different possibilities to realize a co-clustering following the user aim.