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Incremental semi-supervised clustering ensemble for high dimensional data clustering

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dc.contributor.authorYu, Zhiwen-
dc.contributor.authorLuo, Peinan-
dc.contributor.authorWu, Si-
dc.contributor.authorHan, Guoqiang-
dc.contributor.authorYou, Jane-
dc.contributor.authorLeung, Hareton-
dc.contributor.authorWong, Hau-San-
dc.contributor.authorZhang, Jun-
dc.date.accessioned2023-12-12T12:30:38Z-
dc.date.available2023-12-12T12:30:38Z-
dc.date.issued2016-06-
dc.identifier.issn1084-4627-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/116318-
dc.description.abstractRecently, cluster ensemble approaches have gained more and more attention [1]-[2], due to useful applications in the areas of pattern recognition, data mining, bioinformatics, and so on. When compared with traditional single clustering algorithms, cluster ensemble approaches are able to integrate multiple clustering solutions obtained from different data sources into a unified solution, and provide a more robust, stable and accurate final result. © 2016 IEEE.-
dc.format.extent2-
dc.language영어-
dc.language.isoENG-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleIncremental semi-supervised clustering ensemble for high dimensional data clustering-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1109/ICDE.2016.7498386-
dc.identifier.scopusid2-s2.0-84980371925-
dc.identifier.wosid000382554200163-
dc.identifier.bibliographicCitation2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp 1484 - 1485-
dc.citation.title2016 IEEE 32nd International Conference on Data Engineering (ICDE)-
dc.citation.startPage1484-
dc.citation.endPage1485-
dc.type.docTypeConference paper-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasssci-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/7498386-
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