  We propose a novel method for multiple clustering that assumes a
co-clustering structure (partitions in both rows and columns of the data
matrix) in each view. The new method is applicable to high-dimensional data. It
is based on a nonparametric Bayesian approach in which the number of views and
the number of feature-/subject clusters are inferred in a data-driven manner.
We simultaneously model different distribution families, such as Gaussian,
Poisson, and multinomial distributions in each cluster block. This makes our
method applicable to datasets consisting of both numerical and categorical
variables, which biomedical data typically do. Clustering solutions are based
on variational inference with mean field approximation. We apply the proposed
method to synthetic and real data, and show that our method outperforms other
multiple clustering methods both in recovering true cluster structures and in
computation time. Finally, we apply our method to a depression dataset with no
true cluster structure available, from which useful inferences are drawn about
possible clustering structures of the data.
