Randomized Algorithms for Fast Bayesian Hierarchical Clustering

@misc{citeulike:2504970, abstract = {We present two new algorithms for fast Bayesian Hierarchical Clustering on large data sets. Bayesian Hierarchical Clustering (BHC) [1] is a method for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. BHC has several advantages over traditional distancebased agglomerative clustering algorithms. It defines a probabilistic model of the data and uses Bayesian hypothesis testing to decide which merges are advantageous and to output the...}, author = {Heller, Katherine A. and Ghahramani, Zoubin }, citeulike-article-id = {2504970}, keywords = {bayesian, clustering, hierarchical}, posted-at = {2008-03-11 00:10:06}, priority = {2}, title = {Randomized Algorithms for Fast Bayesian Hierarchical Clustering}, url = {http://citeseer.ist.psu.edu/735851.html} }

TY - GEN ID - citeulike:2504970 L3 - citeulike-article-id:2504970 N2 - We present two new algorithms for fast Bayesian Hierarchical Clustering on large data sets. Bayesian Hierarchical Clustering (BHC) [1] is a method for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. BHC has several advantages over traditional distancebased agglomerative clustering algorithms. It defines a probabilistic model of the data and uses Bayesian hypothesis testing to decide which merges are advantageous and to output the... TI - Randomized Algorithms for Fast Bayesian Hierarchical Clustering KW - bayesian KW - clustering KW - hierarchical AU - Heller, Katherine AU - Ghahramani, Zoubin UR - http://citeseer.ist.psu.edu/735851.html ER -