Likelihood based hierarchical clustering

TitleLikelihood based hierarchical clustering
Publication TypeConference Proceedings
Year of Publication2004
AuthorsCastro, R., M. J. Coates, and R. D. Nowak
Conference NameIEEE Trans. on Signal Processing
Volume52
Number of Volumes8
Pagination2308- 2321
Date Published08/2004
KeywordsMarkov Chain Monte Carlo methods, Model-based clustering, network topology identification, tree models
Abstract

This paper develops a new method for hierarchical clustering. Unlike other existing clustering schemes, our method is based on a generative, tree-structured model that represents relationships between the objects to be clustered, rather than directly modeling properties of objects themselves. In certain problems, this generative model naturally captures the physical mechanisms responsible for relationships among objects, for example, in certain evolutionary tree problems in genetics
and communication network topology identification. The paper examines the networking problem in some detail, to illustrate the new clustering method. More broadly, the generative model may not reflect actual physical mechanisms, but it nonetheless provides a means for dealing with errors in the similarity matrix, simultaneously promoting two desirable features in clustering: intra-class similarity and inter-class dissimilarity.

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