Efficient hierarchical clustering for continuous data

Learning hierarchical structures from observed data is a common practice in many knowledge domains. Examples include phylogenies and signaling pathways in biology, language models in linguistics, etc. Agglomerative clustering is still the most popular approach to hierarchical clustering due to its efficiency, ease of implementation and a wide range of possible distance metrics. However, because it is algorithmic in nature, there is no principled way to that agglomerative clustering can be used as a building block in more complex models. Bayesian priors for structure learning on the other hand, are perfectly suited to be employed in larger models. As an example, several authors have proposed using hierarchical structure priors to model correlation in factor models (Rai and Daume III, 2009; Henao et al., 2012; Zhang et al., 2011). Ricardo Henao is Postdoctoral Associate and Joseph E. Lucas is Assistant Research Professor at the Institute for Genome Sciences and Policy (IGSP), Duke University, Durham, NC 27710.