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• W2169657274 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." @default.
• W2169657274 created "2016-06-24" @default.
• W2169657274 creator A5064414755 @default.
• W2169657274 creator A5090140110 @default.
• W2169657274 date "2007-09-01" @default.
• W2169657274 modified "2023-10-16" @default.
• W2169657274 title "Model-Based Clustering With Dissimilarities: A Bayesian Approach" @default.
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• W2169657274 doi "https://doi.org/10.1198/106186007x236127" @default.
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