Likelihood based hierarchical clustering

R.M. Castro, M. Coates, R. Nowak

    Research output: Contribution to journalArticleAcademicpeer-review

    72 Citations (Scopus)
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    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: intraclass similarity and interclass dissimilarity.
    Original languageEnglish
    Pages (from-to)2308-2321
    Number of pages14
    JournalIEEE Transactions on Signal Processing
    Issue number8
    Publication statusPublished - 2004


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