Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Algorithms for Model-Based Gaussian Hierarchical Clustering
SIAM Journal on Scientific Computing
Model Selection in Unsupervised Learning with Applications To Document Clustering
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Using Machine Learning to Improve Information Access
Using Machine Learning to Improve Information Access
The cluster-abstraction model: unsupervised learning of topic hierarchies from text data
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Bayesian classification with correlation and inheritance
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Finding uninformative features in binary data
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
ESPClust: an effective skew prevention method for model-based document clustering
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
MMPClust: a skew prevention algorithm for model-based document clustering
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
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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 by using the marginal likelihood automatically determines the optimal model structure including number of clusters, the depth of the tree and the subset of features to be modeled as having a common distribution at each node. We present experimental results on both synthetic data and a real document collection.