Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2005
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dc.contributor.authorKarimov, Jeyhun-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.date.accessioned2019-07-10T14:42:45Z
dc.date.available2019-07-10T14:42:45Z
dc.date.issued2015
dc.identifier.citationKarimov, J., & Ozbayoglu, M. (2015). Clustering quality improvement of k-means using a hybrid evolutionary model. Procedia Computer Science, 61, 38-45.en_US
dc.identifier.issn1877-0509
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1877050915029737?via%3Dihub-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/2005-
dc.descriptionComplex Adaptive Systems (2015 : San Jose; United States)
dc.description.abstractChoosing good candidates for the initial centroid selection process for compact clustering algorithms, such as k-means, is essential for clustering quality and performance. In this study, a novel hybrid evolutionary model for k-means clustering (HE-kmeans) is proposed. This model uses meta-heuristic methods to identify the "good candidates" for initial centroid selection in k-means clustering method. The results indicate that the clustering quality is improved by approximately 30% compared to the standard random selection of initial centroids. We also experimentally compare our method with the other heuristics proposed for initial centroid selection and the experimental results show that our method performs better in most cases. (C) 2015 The Authors. Published by Elsevier B.V.en_US
dc.language.isoenen_US
dc.publisherELSEVIER Science BVen_US
dc.relation.ispartofProcedia Computer Scienceen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectclusteringen_US
dc.subjectk-meansen_US
dc.subjectcluster-centroidsen_US
dc.subjectPSOen_US
dc.subjectSimulated Annealingen_US
dc.subjectScatter Searchen_US
dc.subjecthybrid modelen_US
dc.subjectdata miningen_US
dc.titleClustering Quality Improvement of k-means using a Hybrid Evolutionary Modelen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume61
dc.identifier.startpage38
dc.identifier.endpage45
dc.authorid0000-0001-7998-5735-
dc.identifier.wosWOS:000373845000005en_US
dc.identifier.scopus2-s2.0-84962720202en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.contributor.YOKid142991-
dc.identifier.doi10.1016/j.procs.2015.09.143-
dc.authorwosidH-2328-2011-
dc.authorscopusid6505999525-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusquality--
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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