A clustering method based on boosting

dc.contributor.authorFrossyniotis, D.en
dc.contributor.authorLikas, A.en
dc.contributor.authorStafylopatis, A.en
dc.date.accessioned2015-11-24T17:00:38Z
dc.date.available2015-11-24T17:00:38Z
dc.identifier.issn0167-8655-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/10795
dc.rightsDefault Licence-
dc.subjectensemble clusteringen
dc.subjectunsupervised learningen
dc.subjectpartitions schemesen
dc.subjectem algorithmen
dc.titleA clustering method based on boostingen
heal.abstractIt is widely recognized that the boosting methodology provides superior results for classification problems. In this paper, we propose the boost-clustering algorithm which constitutes a novel clustering methodology that exploits the general principles of boosting in order to provide a consistent partitioning of a dataset. The boost-clustering algorithm is a multi-clustering method. At each boosting iteration, a new training set is created using weighted random sampling from the original dataset and a simple clustering algorithm (e.g. k-means) is applied to provide a new data partitioning. The final clustering solution is produced by aggregating the multiple clustering results through weighted voting. Experiments on both artificial and real-world data sets indicate that boost-clustering provides solutions of improved quality. (C) 2004 Elsevier B.V. All rights reserved.en
heal.accesscampus-
heal.fullTextAvailabilityTRUE-
heal.identifier.primaryDOI 10.1016/j.patrec.2003.12.018-
heal.journalNamePattern Recognition Lettersen
heal.journalTypepeer reviewed-
heal.languageen-
heal.publicationDate2004-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.typejournalArticle-
heal.type.elΆρθρο Περιοδικούel
heal.type.enJournal articleen

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