  The task of clustering a set of objects based on multiple sources of data
arises in several modern applications. We propose an integrative statistical
model that permits a separate clustering of the objects for each data source.
These separate clusterings adhere loosely to an overall consensus clustering,
and hence they are not independent. We describe a computationally scalable
Bayesian framework for simultaneous estimation of both the consensus clustering
and the source-specific clusterings. We demonstrate that this flexible approach
is more robust than joint clustering of all data sources, and is more powerful
than clustering each data source separately. This work is motivated by the
integrated analysis of heterogeneous biomedical data, and we present an
application to subtype identification of breast cancer tumor samples using
publicly available data from The Cancer Genome Atlas. Software is available at
http://people.duke.edu/~el113/software.html.
