Uncertainty in Artificial Intelligence
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Bayesian Rose Trees
Charles Blundell, Yee Whye Teh, Katherine Heller
Abstract:
Hierarchical structure is ubiquitous in data across many domains. There are many hier- archical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these meth- ods limit discoverable hierarchies to those with binary branching structure. This lim- itation, while computationally convenient, is often undesirable. In this paper we ex- plore a Bayesian hierarchical clustering algo- rithm that can produce trees with arbitrary branching structure at each node, known as rose trees. We interpret these trees as mixtures over partitions of a data set, and use a computationally efficient, greedy ag- glomerative algorithm to find the rose trees which have high marginal likelihood given the data. Lastly, we perform experiments which demonstrate that rose trees are better models of data than the typical binary trees returned by other hierarchical clustering algorithms.
Keywords:
Pages: 65-72
PS Link:
PDF Link: /papers/10/p65-blundell.pdf
BibTex:
@INPROCEEDINGS{Blundell10,
AUTHOR = "Charles Blundell and Yee Whye Teh and Katherine Heller",
TITLE = "Bayesian Rose Trees",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "65--72"
}


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