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dc.contributor.author유재근-
dc.date.accessioned2020-12-24T16:30:17Z-
dc.date.available2020-12-24T16:30:17Z-
dc.date.issued2020-
dc.identifier.issn2287-7843-
dc.identifier.otherOAK-28513-
dc.identifier.urihttp://dspace.ewha.ac.kr/handle/2015.oak/255841-
dc.description.abstractThe K-means clustering algorithm has had successful application in sufficient dimension reduction. Unfortunately, the algorithm does have reproducibility and nestness, which will be discussed in this paper. These are clear deficits for the K-means clustering algorithm; however, the hierarchical clustering algorithm has both reproducibility and nestness, but intensive comparison between K-means and hierarchical clustering algorithm has not yet been done in a sufficient dimension reduction context. In this paper, we rigorously study the two clustering algorithms for two popular sufficient dimension reduction methodology of inverse mean and clustering mean methods throughout intensive numerical studies. Simulation studies and two real data examples confirm that the use of hierarchical clustering algorithm has a potential advantage over the K-means algorithm. © 2020 The Korean Statistical Society, and Korean International Statistical Society.-
dc.languageEnglish-
dc.publisherKorean Statistical Society-
dc.subjectCentral subspace-
dc.subjectHierarchical clustering-
dc.subjectInformative predictor subspace-
dc.subjectK-means clustering-
dc.subjectMultivariate slicing-
dc.subjectSufficient dimension reduction-
dc.titleOn hierarchical clustering in sufficient dimension reduction-
dc.typeArticle-
dc.relation.issue4-
dc.relation.volume27-
dc.relation.indexSCOPUS-
dc.relation.indexKCI-
dc.relation.startpage431-
dc.relation.lastpage443-
dc.relation.journaltitleCommunications for Statistical Applications and Methods-
dc.identifier.doi10.29220/CSAM.2020.27.4.431-
dc.identifier.scopusid2-s2.0-85090581451-
dc.author.googleYoo C.-
dc.author.googleYoo Y.-
dc.author.googleUm H.Y.-
dc.author.googleYoo J.K.-
dc.contributor.scopusid유재근(23032759600)-
dc.date.modifydate20201224145425-
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자연과학대학 > 통계학전공 > Journal papers
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