Likelihood based hierarchical clustering

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

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    68 Citaten (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.
    Originele taal-2Engels
    Pagina's (van-tot)2308-2321
    Aantal pagina's14
    TijdschriftIEEE Transactions on Signal Processing
    Nummer van het tijdschrift8
    StatusGepubliceerd - 2004


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