Latent Dirichlet Co-Clustering

Abstract

We present a generative model for simultaneously clustering documents and terms. Our model is a four-level hierarchical Bayesian model, in which each document is modeled as a random mixture of document topics , where each topic is a distribution over some segments of the text. Each of these segments in the document can be modeled as a mixture of word topics where each topic is a distribution over words. We present efficient approximate inference techniques based on Markov Chain Monte Carlo method and a moment-matching algorithm for empirical Bayes parameter estimation. We report results in document modeling, document and term clustering, comparing to other topic models, Clustering and Co-Clustering algorithms including latent Dirichlet allocation (LDA), model-based overlapping clustering (MOC), model-based overlapping co-clustering (MOCC) and information-theoretic co-clustering (ITCC).