Bayesian consensus clustering.

ABSTRACT:

Motivation

In biomedical research a growing number of platforms and technologies are used to measure diverse but related information, and the task of clustering a set of objects based on multiple sources of data arises in several applications. Most current approaches to multisource clustering either independently determine a separate clustering for each data source or determine a single 'joint' clustering for all data sources. There is a need for more flexible approaches that simultaneously model the dependence and the heterogeneity of the data sources.

Results

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

SUBMITTER: Lock EF 

PROVIDER: S-EPMC3789539 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature