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dc.contributor.authorLi, Jiuyongen_US
dc.contributor.authorShen, Hongen_US
dc.contributor.authorTopor, Rodneyen_US
dc.contributor.editorLarry Kerschberg, Maria Zemankova, Zbigniew Rasen_US
dc.date.accessioned2017-04-24T09:05:02Z
dc.date.available2017-04-24T09:05:02Z
dc.date.issued2004en_US
dc.date.modified2010-08-17T05:03:42Z
dc.identifier.issn09259902en_US
dc.identifier.doi10.1023/B:JIIS.0000012468.25883.a5en_AU
dc.identifier.urihttp://hdl.handle.net/10072/5151
dc.description.abstractMining transaction databases for association rules usually generates a large number of rules, most of which are unnecessary when used for subsequent prediction. In this paper we define a rule set for a given transaction database that is much smaller than the association rule set but makes the same predictions as the association rule set by the confidence priority. We call this rule set informative rule set. The informative rule set is not constrained to particular target items; and it is smaller than the non-redundant association rule set. We characterise relationships between the informative rule set and non-redundant association rule set. We present an algorithm to directly generate the informative rule set without generating all frequent itemsets first that accesses the database less frequently than other direct methods. We show experimentally that the informative rule set is much smaller and can be generated more efficiently than both the association rule set and non-redundant association rule seten_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherKluwer Academic Publishersen_US
dc.publisher.placeUSAen_US
dc.relation.ispartofpagefrom155en_US
dc.relation.ispartofpageto174en_US
dc.relation.ispartofissue2en_US
dc.relation.ispartofjournalJournal of Intelligent Information Systemsen_US
dc.relation.ispartofvolume22en_US
dc.subject.fieldofresearchcode289999en_US
dc.subject.fieldofresearchcode280399en_US
dc.titleMining Informative Rule Set for Predictionen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.date.issued2004
gro.hasfulltextNo Full Text


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