Yee Whye Teh (UCL)
Bayesian Rose Trees
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 each node, known as rose trees. We interpret these trees as mixtures
over partitions of a data set, and use a computationally efficient, greedy agglomerative
algorithm optimizing likelihood ratios to find a rose tree 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.