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

ABSTRACT: We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the pr

SUBMITTER: Tokuda T 

PROVIDER: S-EPMC5648298 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature