Applied Comparison of DBSCAN, OPTICS and K-Means Clustering Algorithms

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dc.authoridBilgin, Turgay Tugay/0000-0002-9245-5728en_US
dc.contributor.authorBilgin, T. T.
dc.contributor.authorCamurcu, Yılmaz
dc.date.accessioned2024-07-12T21:37:27Z
dc.date.available2024-07-12T21:37:27Z
dc.date.issued2005en_US
dc.department[Belirlenecek]en_US
dc.description.abstractDBSCAN and OPTICS are two recent clustering algorithms on data mining. In this study, these two algorithms and K-means which is one of the oldest clustering algorithms are compared. Comparison is based on cluster discovery performance on synthetic database. Consequently, two recent clustering algorithms DBSCAN and OPTICS are performed superior accuracy and cluster discovery ability over K-means algorithm.en_US
dc.identifier.endpage145en_US
dc.identifier.issn1302-0900
dc.identifier.issn2147-9429
dc.identifier.issue2en_US
dc.identifier.startpage139en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12415/6773
dc.identifier.volume8en_US
dc.identifier.wosWOS:000447756900003en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.language.isotren_US
dc.publisherGazi Univen_US
dc.relation.ispartofJournal of Polytechnic-Politeknik Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKY04115
dc.subjectData Miningen_US
dc.subjectClustering Analysisen_US
dc.subjectDbscanen_US
dc.subjectOpticsen_US
dc.subjectK-Meansen_US
dc.titleApplied Comparison of DBSCAN, OPTICS and K-Means Clustering Algorithmsen_US
dc.typeArticle
dspace.entity.typePublication

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