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dc.contributor.authorHunt, Lynette Anne
dc.contributor.authorJorgensen, Murray A.
dc.date.accessioned2011-08-07T21:37:55Z
dc.date.available2011-08-07T21:37:55Z
dc.date.issued2011
dc.identifier.citationHunt, L.A. & Jorgensen, M.A. (2011). Clustering mixed data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(4), 352-361.en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/5551
dc.description.abstractMixture model clustering proceeds by fitting a finite mixture of multivariate distributions to data, the fitted mixture density then being used to allocate the data to one of the components. Common model formulations assume that either all the attributes are continuous or all the attributes are categorical. In this paper, we consider options for model formulation in the more practical case of mixed data: multivariate data sets that contain both continuous and categorical attributes.en_NZ
dc.language.isoen
dc.publisherWileyen_NZ
dc.relation.urihttp://onlinelibrary.wiley.com/doi/10.1002/widm.33/abstracten_NZ
dc.subjectmixture model clusteringen_NZ
dc.subjectdataen_NZ
dc.titleClustering mixed dataen_NZ
dc.typeJournal Articleen_NZ
dc.identifier.doi10.1002/widm.33en_NZ
dc.relation.isPartOfWIRE's Data Mining Knowledge Discoveryen_NZ
pubs.begin-page352en_NZ
pubs.editionJuly/Augusten_NZ
pubs.elements-id35878
pubs.end-page361en_NZ
pubs.issue4en_NZ
pubs.volume1en_NZ
uow.identifier.article-no4en_NZ


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