A co-clustering algorithm for large sparse binary data matrices, based on a greedy technique and enriched with a local search strategy to escape poor local maxima, is proposed. The algorithm starts with an initial random solution and searches for a locally optimal solution by successive transformations that improve a quality function which combines row and column means together with the size of the co-cluster. Experimental results on synthetic and real data sets show that the method is able to find significant co-clusters.