DC Field | Value | Language |
---|---|---|
dc.contributor.author | Doo, Woojin | ko |
dc.contributor.author | Kim, Heeyoung | ko |
dc.date.accessioned | 2021-08-10T07:50:43Z | - |
dc.date.available | 2021-08-10T07:50:43Z | - |
dc.date.created | 2021-04-26 | - |
dc.date.created | 2021-04-26 | - |
dc.date.issued | 2021-08 | - |
dc.identifier.citation | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v.91, no.12, pp.2551 - 2568 | - |
dc.identifier.issn | 0094-9655 | - |
dc.identifier.uri | http://hdl.handle.net/10203/287118 | - |
dc.description.abstract | When clustering high-dimensional data, it is often important to identify variables that discriminate the clusters. Meanwhile, a common issue in clustering is to determine the number of clusters. In this study, we propose a new method that simultaneously performs clustering and variable selection, while inferring the number of clusters from the data. We formulate the clustering problem using a finite mixture model with a symmetric Dirichlet weights prior, while also placing a prior on the number of components. That is, we utilize a mixture of finite mixtures. We handle the variable selection problem by introducing a latent binary vector, which represents the inclusion/exclusion of variables. We update the binary vector for variable selection using a Metropolis algorithm and perform inference on the cluster structure using a split-merge Markov chain Monte Carlo technique. We demonstrate the advantage of our method using simulated and two real DNA microarray datasets. | - |
dc.language | English | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.title | Bayesian variable selection in clustering high-dimensional data via a mixture of finite mixtures | - |
dc.type | Article | - |
dc.identifier.wosid | 000635014000001 | - |
dc.identifier.scopusid | 2-s2.0-85103409603 | - |
dc.type.rims | ART | - |
dc.citation.volume | 91 | - |
dc.citation.issue | 12 | - |
dc.citation.beginningpage | 2551 | - |
dc.citation.endingpage | 2568 | - |
dc.citation.publicationname | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | - |
dc.identifier.doi | 10.1080/00949655.2021.1902526 | - |
dc.contributor.localauthor | Kim, Heeyoung | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Bayesian inference | - |
dc.subject.keywordAuthor | clustering | - |
dc.subject.keywordAuthor | DNA microarray data | - |
dc.subject.keywordAuthor | finite mixture model | - |
dc.subject.keywordAuthor | high-dimensional data | - |
dc.subject.keywordAuthor | variable selection | - |
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