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dc.contributor.authorLikas, A.en
dc.contributor.authorVlassis, N.en
dc.contributor.authorVerbeek, J. J.en
dc.date.accessioned2015-11-24T17:00:24Z-
dc.date.available2015-11-24T17:00:24Z-
dc.identifier.issn0031-3203-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/10762-
dc.rightsDefault Licence-
dc.subjectclusteringen
dc.subjectk-means algorithmen
dc.subjectglobal optimizationen
dc.subjectk-d treesen
dc.subjectdata miningen
dc.subjecttreesen
dc.titleThe global k-means clustering algorithmen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.publicationDate2003-
heal.abstractWe present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) executions of the k-means algorithm from suitable initial positions. We also propose modifications of the method to reduce the computational load without significantly affecting solution quality. The proposed clustering methods are tested on well-known data sets and they compare favorably to the k-means algorithm with random restarts. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.en
heal.journalNamePattern Recognitionen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

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