2008 ModelbasedDocumentClusteringwit

• (Walker et al., 2008) ⇒ Daniel David Walker, and Eric K. Ringger. (2008). “Model-based Document Clustering with a Collapsed Gibbs Sampler.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401975

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• http://scholar.google.com/scholar?q=%22Model-based+document+clustering+with+a+collapsed+gibbs+sampler%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401975&preflayout=flat#citedby

Quotes

Abstract

Model-based algorithms are emerging as a preferred method for document clustering. As computing resources improve, methods such as Gibbs sampling have become more common for parameter estimation in these models. Gibbs sampling is well understood for many applications, but has not been extensively studied for use in document clustering. We explore the convergence rate, the possibility of label switching, and chain summarization methodologies for document clustering on a particular model, namely a mixture of multinomials model, and show that fairly simple methods can be employed, while still producing clusterings of superior quality compared to those produced with the EM algorithm.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 ModelbasedDocumentClusteringwit	Daniel David Walker Eric K. Ringger			Model-based Document Clustering with a Collapsed Gibbs Sampler				10.1145/1401890.1401975