Model-Based Hierarchical Clustering (2000)
| Venue: | In Proc. 16th Conf. Uncertainty in Artificial Intelligence |
| Citations: | 21 - 0 self |
BibTeX
@INPROCEEDINGS{Vaithyanathan00model-basedhierarchical,
author = {Shivakumar Vaithyanathan and Byron Dom},
title = {Model-Based Hierarchical Clustering},
booktitle = {In Proc. 16th Conf. Uncertainty in Artificial Intelligence},
year = {2000},
pages = {599--608},
publisher = {UAI}
}
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Abstract
We present an approach to model-based hierarchical clustering by formulating an objective function based on a Bayesian analysis. This model organizes the data into a cluster hierarchy while specifying a complex feature-set partitioning that is a key component of our model. Features can have either a unique distribution in every cluster or a common distribution over some (or even all) of the clusters. The cluster subsets over which these features have such a common distribution correspond to the nodes (clusters) of the tree representing the hierarchy. We apply this general model to the problem of document clustering for which we use a multinomial likelihood function and Dirichlet priors. Our algorithm consists of a two-stage process wherein we first perform a flat clustering followed by a modified hierarchical agglomerative merging process that includes determining the features that will have common distributions over the merged clusters. The regularization induced...
