Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7257
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dc.contributor.authorOzyer, Tansel-
dc.contributor.authorAlhajj, Reda-
dc.date.accessioned2021-09-11T15:56:08Z-
dc.date.available2021-09-11T15:56:08Z-
dc.date.issued2009en_US
dc.identifier.issn0924-669X-
dc.identifier.issn1573-7497-
dc.identifier.urihttps://doi.org/10.1007/s10489-008-0129-8-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/7257-
dc.description.abstractThis paper applies divide and conquer approach in an iterative way to handle the clustering process. The target is a parallelized effective and efficient approach that produces the intended clustering result. We achieve scalability by first partitioning a large dataset into subsets of manageable sizes based on the specifications of the machine to be used in the clustering process; then cluster the partitions separately in parallel. The centroid of each obtained cluster is treated like the root of a tree with instances in its cluster as leaves. The partitioning and clustering process is iteratively applied on the centroids with the trees growing up until we get the final clustering; the outcome is a forest with one tree per cluster. Finally, a conquer process is performed to get the actual intended clustering, where each instance (leaf node) belongs to the final cluster represented by the root of its tree. We use multi-objective genetic algorithm combined with validity indices to decide on the number of classes. This approach fits well for interactive online clustering. It facilitates for incremental clustering because chunks of instances are clustered as stand alone sets, and then the results are merged with existing clusters. This is attractive and feasible because we consider the clustering of only centroids after the first clustering stage. The reported test results demonstrate the applicability and effectiveness of the proposed approach.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofApplied Intelligenceen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClusteringen_US
dc.subjectData miningen_US
dc.subjectMulti-objective optimizationen_US
dc.subjectValidity analysisen_US
dc.subjectDivide and conqueren_US
dc.subjectParallelismen_US
dc.subjectIncremental clusteringen_US
dc.titleParallel Clustering of High Dimensional Data by Integrating Multi-Objective Genetic Algorithm With Divide and Conqueren_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume31en_US
dc.identifier.issue3en_US
dc.identifier.startpage318en_US
dc.identifier.endpage331en_US
dc.identifier.wosWOS:000272157900012en_US
dc.identifier.scopus2-s2.0-73149095839en_US
dc.institutionauthorÖzyer, Tansel-
dc.identifier.doi10.1007/s10489-008-0129-8-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ3-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
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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