Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5549
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dc.contributor.authorÖzyer T.-
dc.contributor.authorAlhajj R.-
dc.date.accessioned2021-09-11T15:19:13Z-
dc.date.available2021-09-11T15:19:13Z-
dc.date.issued2006en_US
dc.identifier.citation2006 3rd International IEEE Conference Intelligent Systems, IS'06, 4 September 2006 through 6 September 2006, London, 72382en_US
dc.identifier.isbn1424401968; 9781424401963-
dc.identifier.issn1541-1672-
dc.identifier.urihttps://doi.org/10.1109/IS.2006.348468-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/5549-
dc.description.abstractClustering is an essential process that leads to the classification of a given set of instances based on user-specified criteria; and different factors may lead to different clustering results. Thus, a large number of clustering algorithms exist to satisfy different purposes. However, scalability and the fact that algorithms in general need the number of clusters be specified apriori, which is mostly hard to estimate even for domain experts, are two challenges that motivate the development of new algorithms. This paper presents a novel approach to handle these two issues. We mainly developed a clustering method that works as an iterative approach to handle the scalability problem; and we utilize multi-objective genetic algorithm combined with validity indexes to decide on the number of clusters. The basic idea is to partition the dataset first; then cluster each partition separately. Finally, each obtained cluster is treated as a single instance (represented by its centroid) and a conquer process is performed to get the final clustering of the complete dataset. Test results on one large real dataset demonstrate the applicability and effectiveness of the proposed approach. © 2006 IEEE.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Intelligent Systemsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectClusteringen_US
dc.subjectData miningen_US
dc.subjectMulti-objective genetic algorithmen_US
dc.subjectPartitioningen_US
dc.subjectValidity indexesen_US
dc.titleAchieving Natural Clustering by Validating Results of Iterative Evolutionary Clustering Approachen_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.startpage488en_US
dc.identifier.endpage493en_US
dc.identifier.scopus2-s2.0-38849167273en_US
dc.institutionauthorÖzyer, Tansel-
dc.identifier.doi10.1109/IS.2006.348468-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.conference2006 3rd International IEEE Conference Intelligent Systems, IS'06en_US
dc.identifier.scopusqualityQ1-
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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