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dc.contributor.authorVogts, Kevinen_US
dc.contributor.authorPope, Nigelen_US
dc.date.accessioned2017-05-03T14:39:27Z
dc.date.available2017-05-03T14:39:27Z
dc.date.issued2004en_US
dc.date.modified2010-08-05T07:16:25Z
dc.identifier.urihttp://hdl.handle.net/10072/33307
dc.description.abstractCluster analysis is a technique used to group objects into clusters such that similar objects are grouped together in the same cluster. Early methods were derived from multivariate statistics. Some newer methods are based on rough sets, introduced by Pawlak [3], [4]. An extension of rough sets to rough clusters was introduced in [5]. The lower approximation (LA) of a rough cluster contains objects that only belong to that cluster, and the upper approximation (UA) contains objects that may belong to more than one cluster. An EA can be used to find a set of lower approximations of rough clusters that provide the most comprehensive coverage of the data set with the minimum number of clusters.en_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringeren_US
dc.publisher.placeUSAen_US
dc.publisher.urihttp://www.sigevo.org/gecco-2004/en_AU
dc.relation.ispartofconferencenameGenetic and Evolutionary Computation • GECCO 2004en_US
dc.relation.ispartofconferencetitleGenetic and Evolutionary Computation Conference Seattle,WA, USA, June 26-30, 2004 Proceedings, Part IIen_US
dc.relation.ispartofdatefrom2004-06-26en_US
dc.relation.ispartofdateto2004-06-30en_US
dc.relation.ispartoflocationSeattleen_US
dc.subject.fieldofresearchMarketing Research Methodologyen_US
dc.subject.fieldofresearchcode150505en_US
dc.titleGenerating compact rough cluster descriptions using an evolutionary algorithmen_US
dc.typeConference outputen_US
dc.type.descriptionE3 - Conference Publications (Extract Paper)en_US
dc.type.codeE - Conference Publicationsen_US
gro.date.issued2004
gro.hasfulltextNo Full Text


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    Contains papers delivered by Griffith authors at national and international conferences.

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