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Model-based clustering with dissimilarities: A Bayesian approach

Title
Model-based clustering with dissimilarities: A Bayesian approach
Authors
Oh M.-S.Raftery A.E.
Ewha Authors
오만숙
SCOPUS Author ID
오만숙scopus
Issue Date
2007
Journal Title
Journal of Computational and Graphical Statistics
ISSN
1061-8600JCR Link
Citation
vol. 16, no. 3, pp. 559 - 585
Indexed
SCIE; SCOPUS WOS scopus
Abstract
A Bayesian model-based clustering method is proposed for clustering objects on the basis of dissimilarites. This combines two basic ideas. The first is that the objects have latent positions in a Euclidean space, and that the observed dissimilarities are measurements of the Euclidean distances with error. The second idea is that the latent positions are generated from a mixture of multivariate normal distributions, each one corresponding to a cluster. We estimate the resulting model in a Bayesian way using Markov chain Monte Carlo. The method carries out multidimensional scaling and model-based clustering simultaneously, and yields good object configurations and good clustering results with reasonable measures of clustering uncertainties. In the examples we study, the clustering results based on low-dimensional configurations were almost as good as those based on high-dimensional ones. Thus, the method can be used as a tool for dimension reduction when clustering high-dimensional objects, which may be useful especially for visual inspection of clusters. We also propose a Bayesian criterion for choosing the dimension of the object configuration and the number of clusters simultaneously. This is easy to compute and works reasonably well in simulations and real examples. © 2007 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
DOI
10.1198/106186007X236127
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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