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dc.contributor.authorLoekito, E
dc.contributor.authorBailey, J
dc.contributor.authorPei, J
dc.date.available2014-05-21T22:53:06Z
dc.date.issued2010-08-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000280251200004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=d4d813f4571fa7d6246bdc0dfeca3a1c
dc.identifier.citationLoekito, E., Bailey, J. & Pei, J. (2010). A binary decision diagram based approach for mining frequent subsequences. KNOWLEDGE AND INFORMATION SYSTEMS, 24 (2), pp.235-268. https://doi.org/10.1007/s10115-009-0252-9.
dc.identifier.issn0219-1377
dc.identifier.urihttp://hdl.handle.net/11343/29315
dc.languageEnglish
dc.publisherSPRINGER LONDON LTD
dc.subjectArtificial Intelligence and Image Processing
dc.titleA binary decision diagram based approach for mining frequent subsequences
dc.typeJournal Article
dc.identifier.doi10.1007/s10115-009-0252-9
melbourne.peerreviewPeer Reviewed
melbourne.affiliationThe University of Melbourne
melbourne.affiliation.departmentComputer Science and Software Engineering
melbourne.source.titleKNOWLEDGE AND INFORMATION SYSTEMS
melbourne.source.volume24
melbourne.source.issue2
melbourne.source.pages235-268
dc.description.pagestart235
melbourne.publicationid146375
melbourne.elementsid323657
melbourne.contributor.authorBailey, James
melbourne.contributor.authorLOEKITO, ELSA
melbourne.internal.ingestnoteAbstract bulk upload (2017-07-20)
dc.identifier.eissn0219-3116
melbourne.accessrightsThis item is currently not available from this repository


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