The Journal of Machine Learning Research
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Latent Dirichlet Co-Clustering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
PAC-Bayesian Analysis of Co-clustering and Beyond
The Journal of Machine Learning Research
Feature enriched nonparametric bayesian co-clustering
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Co-clustering has emerged as an important technique for mining contingency data matrices. However, almost all existing co-clustering algorithms are hard partitioning, assigning each row and column of the data matrix to one cluster. Recently a Bayesian co-clustering approach has been proposed which allows a probability distribution membership in row and column clusters. The approach uses variational inference for parameter estimation. In this work, we modify the Bayesian co-clustering model, and use collapsed Gibbs sampling and collapsed variational inference for parameter estimation. Our empirical evaluation on real data sets shows that both collapsed Gibbs sampling and collapsed variational inference are able to find more accurate likelihood estimates than the standard variational Bayesian co-clustering approach.