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We propose a novel method for multiple clustering that assumes aco-clustering structure (partitions in both rows and columns of the datamatrix) in each view. The new method is applicable to high-dimensional data. Itis based on a nonparametric Bayesian approach in which the number of views andthe 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 ourmethod applicable to datasets consisting of both numerical and categoricalvariables, which biomedical data typically do. Clustering solutions are basedon variational inference with mean field approximation. We apply the proposedmethod to synthetic and real data, and show that our method outperforms othermultiple clustering methods both in recovering true cluster structures and incomputation time. Finally, we apply our method to a depression dataset with notrue cluster structure available, from which useful inferences are drawn aboutpossible clustering structures of the data.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions