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Bayesian rose trees

Abstract:

Hierarchical structure is ubiquitous in data across many domains. There are many hierarchical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these methods limit discoverable hierarchies to those with binary branching structure. This limitation, while computationally convenient, is often undesirable. In this paper we explore a Bayesian hierarchical clustering algorithm that can produce trees with arbitrary branching structure at...

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Publication status:
Published

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Authors


Blundell, C More by this author
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Heller, KA More by this author
Journal:
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010
Pages:
65-72
Publication date:
2010-12-01
URN:
uuid:958397f6-58c5-400e-b730-aad8152a1b56
Source identifiers:
353227
Local pid:
pubs:353227

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