Full metadata record
DC Field | Value | Language |
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dc.contributor.author | 오만숙 | - |
dc.date.accessioned | 2017-01-18T02:01:35Z | - |
dc.date.available | 2017-01-18T02:01:35Z | - |
dc.date.issued | 2007 | - |
dc.identifier.issn | 1061-8600 | - |
dc.identifier.other | OAK-4297 | - |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/234020 | - |
dc.description.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. | - |
dc.language | English | - |
dc.title | Model-based clustering with dissimilarities: A Bayesian approach | - |
dc.type | Article | - |
dc.relation.issue | 3 | - |
dc.relation.volume | 16 | - |
dc.relation.index | SCIE | - |
dc.relation.index | SCOPUS | - |
dc.relation.startpage | 559 | - |
dc.relation.lastpage | 585 | - |
dc.relation.journaltitle | Journal of Computational and Graphical Statistics | - |
dc.identifier.doi | 10.1198/106186007X236127 | - |
dc.identifier.wosid | WOS:000249591000003 | - |
dc.identifier.scopusid | 2-s2.0-35349026099 | - |
dc.author.google | Oh M.-S. | - |
dc.author.google | Raftery A.E. | - |
dc.contributor.scopusid | 오만숙(7201600334) | - |
dc.date.modifydate | 20230411111253 | - |