Copyright SIAM. We consider the mining of hidden block structures from time-varying data using evolutionary co-clustering. Existing methods are based on the spectral learning framework, thus lacking a probabilistic interpretation. To overcome this limitation, we develop a probabilistic model for evolutionary co-clustering in this paper. The proposed model assumes that the observed data are generated via a two-step process that depends on the historic co-clusters, thereby capturing the temporal smoothness in a probabilistically principled manner. We develop an EM algorithm to perform maximum likelihood parameter estimation. An appealing feature of the proposed probabilistic model is that it leads to soft co-clustering assignments naturally. To the best of our knowledge, our work represents the first attempt to perform evolutionary soft co-clustering. We evaluate the proposed method on both synthetic and real data sets. Experimental results show that our method consistently outperforms prior approaches based on spectral method.
name of conference
Proceedings of the 2013 SIAM International Conference on Data Mining