Stochastic Co-clustering for Document-Term Data

International audienceCo-clustering is more useful than one-sided clustering when dealing with high dimensional sparse data. We propose to address the aim of document clustering with a generative model-based co-clustering approach. To this end, we rely on a particular mixture of von Mises-Fisher distributions and propose a new parsimonious model allowing to reveal a block diagonal structure as well as a good partitioning of documents and terms. Then, by setting the estimate of the model parameters under the maximum likelihood (ML) approach, we derive three novel co-clustering algorithms: a soft one and two stochastic variants. Empirical results on numerous simulated and real-world datasets, demonstrate the advantages of our approach to model and co-cluster high dimensional sparse data