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1、ClassifyingTextDocumentsbyAssociatingTermswithTextCategories*OsmarR.Za'faneMaria-LuizaAntonieDepartmentofComputingScienceUniversityofAlbertaEdmonton,Alberta,Canada.{zaiane,luiza}@cs.ualberta.caAbstractoutatonepoint,therapidgrowthoftheWebhasrevivedtheinterestintextcategorization.ThepastAutoma
2、tictextcategorizationhasalwaysbeenanimportantdecadehasseenanincreasingeffortinapplyingnewapplicationandresearchtopicsincetheinceptionofdigitaltechniquesindiscriminatingandclassifyingtextdoc-documents.Today,textcategorizationisanecessityduetotheuments.Atextcategorizationsystemcanbeusedverylar
3、geamountoftextdocumentsthatwehavetodealwithtoclassifye-mailmessages(e-mailresponsesystems),daily.Manytechniquesandalgorithmsforautomatictextcat-incomingmemos,tofilterdocuments,toroutetextsegorizationhavebeendevisedandproposedintheliterature.ortoclassifywebpagesinaYahoo-likemanner.TheHowever,
4、thereisstillmuchroomforimprovingtheeffective-increasingnumberoftheonlinedocumentshasde-nessoftheseclassifiers,andnewmodelsneedtobeexamined.mandedmoreresearchinthetextcategorizationfield.Weproposehereinanewapproachforautomatictextcatego-Manytechniqueshavebeenappliedintextcate-rization.Thispap
5、erexplorestheuseofassociationrulemininggorization,suchasBayesianNetworks,decisiontrees,inbuildingatextcategorizationsystemandproposesanewfastneuralnetworks,supportvectormachines,k-nearestalgorithmforbuildingatextclassifier.Ourapproachhastheneighborapproach,etc.Agoodsurveyonthesemeth-advantag
6、eofaveryfasttrainingphase,andtherulesoftheodsandtheirapplicationintextcategorizationcanbeclassifiergeneratedareeasytounderstandandmanuallytune-foundin[19].able.OurinvestigationleadstoconcludethatassociationruleAnotheraspectthatmotivatesourresearchistheminingisagoodandpromisingstrategyforeffi
7、cientautomaticsuccessofassociationrulemininginthedataminingtextcategorization.researchcommunity.TheuseofassociationruleshasbeenexploitedforfindingnewandinterestinghiddenKeywords:TextCategorization,Classification,Asso-patternsinlargetransactionaldat
