Co-clustering for Binary Data with Maximum Modularity

The modularity measure have been recently proposed for graph clustering which allows automatic selection of the number of clusters. Empirically, higher values of the modularity measure have been shown to correlate well with graph clustering. In order to tackle the co-clustering problem for binary data, we propose a generalized modularity measure and a spectral approximation of the modularity matrix. A spectral algorithm maximizing the modularity measure is then presented to search for the row and column clusters simultaneously. Experimental results are performed on a variety of real-world data sets confirming the interest of the use of the modularity.