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dc.contributor.author임용빈-
dc.date.accessioned2016-08-28T12:08:05Z-
dc.date.available2016-08-28T12:08:05Z-
dc.date.issued2009-
dc.identifier.issn0266-4763-
dc.identifier.otherOAK-5170-
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/220082-
dc.description.abstractThe aim of this study is to assign weights w 1,..., wm to m clustering variables Z 1,..., Z m, so that k groups were uncovered to reveal more meaningful within-group coherence. We propose a new criterion to be minimized, which is the sum of the weighted within-cluster sums of squares and the penalty for the heterogeneity in variable weights w 1,..., w m. We will present the computing algorithm for such k-means clustering, a working procedure to determine a suitable value of penalty constant and numerical examples, among which one is simulated and the other two are real.-
dc.languageEnglish-
dc.titleWeighting variables in K-means clustering-
dc.typeArticle-
dc.relation.issue1-
dc.relation.volume36-
dc.relation.indexSCIE-
dc.relation.indexSCOPUS-
dc.relation.startpage67-
dc.relation.lastpage78-
dc.relation.journaltitleJournal of Applied Statistics-
dc.identifier.doi10.1080/02664760802382533-
dc.identifier.wosidWOS:000260573200007-
dc.identifier.scopusid2-s2.0-56049111681-
dc.author.googleHuh M.-H.-
dc.author.googleLim Y.-
dc.contributor.scopusid임용빈(24370019400)-
dc.date.modifydate20230210130732-
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자연과학대학 > 통계학전공 > Journal papers
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