BHC: Bayesian Hierarchical Clustering
Version 1.28.0

The method performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. This avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. This implementation accepts multinomial (i.e. discrete, with 2+ categories) or time-series data. This version also includes a randomised algorithm which is more efficient for larger data sets.

Package details

AuthorRich Savage, Emma Cooke, Robert Darkins, Yang Xu
Bioconductor views Clustering Microarray
MaintainerRich Savage <>
Package repositoryView on Bioconductor
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BHC documentation built on May 31, 2017, 2:30 p.m.