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dc.contributor.authorVinh, NX
dc.contributor.authorEpps, J
dc.contributor.authorBailey, J
dc.date.available2014-05-21T22:52:31Z
dc.date.issued2010-10-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000284040000008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=d4d813f4571fa7d6246bdc0dfeca3a1c
dc.identifier.citationVinh, N. X., Epps, J. & Bailey, J. (2010). Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance. JOURNAL OF MACHINE LEARNING RESEARCH, 11, pp.2837-2854
dc.identifier.issn1532-4435
dc.identifier.urihttp://hdl.handle.net/11343/29309
dc.languageEnglish
dc.publisherMICROTOME PUBL
dc.subjectArtificial Intelligence and Image Processing
dc.titleInformation Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance
dc.typeJournal Article
melbourne.peerreviewPeer Reviewed
melbourne.affiliationThe University of Melbourne
melbourne.affiliation.departmentComputer Science and Software Engineering
melbourne.source.titleJournal of Machine Learning Research
melbourne.source.volume11
melbourne.source.pages2837-2854
dc.description.pagestart2837
melbourne.publicationid150010
melbourne.elementsid326040
melbourne.contributor.authorBailey, James
melbourne.contributor.authorNguyen, Xuan
dc.identifier.eissn1533-7928
melbourne.accessrightsThis item is currently not available from this repository


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