  Biclustering is a class of techniques that simultaneously clusters the rows
and columns of a matrix to sort heterogeneous data into homogeneous blocks.
Although many algorithms have been proposed to find biclusters, existing
methods suffer from the pre-specification of the number of biclusters or place
constraints on the model structure. To address these issues, we develop a
novel, non-parametric probabilistic biclustering method based on Dirichlet
processes to identify biclusters with strong co-occurrence in both rows and
columns. The proposed method utilizes dual Dirichlet process mixture models to
learn row and column clusters, with the number of resulting clusters determined
by the data rather than pre-specified. Probabilistic biclusters are identified
by modeling the mutual dependence between the row and column clusters. We apply
our method to two different applications, text mining and gene expression
analysis, and demonstrate that our method improves bicluster extraction in many
settings compared to existing approaches.
