Likelihood based hierarchical clustering

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 cer...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2004-08, Vol.52 (8), p.2308-2321
Main Authors: Castro, R.M., Coates, M.J., Nowak, R.D.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Clustering
Clustering algorithms
Clustering methods
Communication networks
Covariance matrix
Dealing
Evolutionary
Genetics
Mathematical models
Measurement errors
Network topology
Process design
Proteins
Signal processing
Similarity
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Summary: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.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2004.831124