Modularity and Spectral Co-Clustering for Categorical Data

To tackle the co-clustering problem on categorical data, we consider a spectral approach. We first define a generalized modularity measure for the co-clustering task. Then, we reformulate its maximization as a trace maximization problem. Finally we develop a spectral based co-clustering algorithm performing this maximization. The proposed algorithm is then capable to cluster rows and colunms simultaneously. Experimental results on synthetic and real data sets confirm the good performance of our algorithm.