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Enhanced Visual Analysis for Cluster Tendency Assessment and Data Partitioning

dc.contributor.authorWang, L
dc.contributor.authorGeng, X
dc.contributor.authorBezdek, J
dc.contributor.authorLeckie, C
dc.contributor.authorRamamohanarao, K
dc.date.available2014-05-21T22:49:19Z
dc.date.issued2010-10-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000281000500005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=d4d813f4571fa7d6246bdc0dfeca3a1c
dc.identifier.citationWang, L., Geng, X., Bezdek, J., Leckie, C. & Ramamohanarao, K. (2010). Enhanced Visual Analysis for Cluster Tendency Assessment and Data Partitioning. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 22 (10), pp.1401-1414. https://doi.org/10.1109/TKDE.2009.192.
dc.identifier.issn1041-4347
dc.identifier.urihttp://hdl.handle.net/11343/29276
dc.languageEnglish
dc.publisherIEEE COMPUTER SOC
dc.subjectArtificial Intelligence and Image Processing
dc.titleEnhanced Visual Analysis for Cluster Tendency Assessment and Data Partitioning
dc.typeJournal Article
dc.identifier.doi10.1109/TKDE.2009.192
melbourne.peerreviewPeer Reviewed
melbourne.affiliationThe University of Melbourne
melbourne.affiliation.departmentComputer Science and Software Engineering
melbourne.source.titleIEEE Transactions on Knowledge and Data Engineering
melbourne.source.volume22
melbourne.source.issue10
melbourne.source.pages1401-1414
dc.description.pagestart1401
melbourne.publicationid146979
melbourne.elementsid324056
melbourne.contributor.authorBezdek, James
melbourne.contributor.authorLeckie, Christopher
melbourne.contributor.authorKotagiri, Ramamohanarao
melbourne.internal.ingestnoteAbstract bulk upload (2017-07-20)
dc.identifier.eissn1558-2191
melbourne.accessrightsThis item is currently not available from this repository


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