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1、南京邮电大学硕士学位论文基于粗糙集的网格海量数据挖掘若干关键技术研究姓名：夏奇思申请学位级别：硕士专业：计算机软件与理论指导教师：茅苏;王汝传2011-03南京邮电大学硕士研究生学位论文ABSTRACTIIABSTRACTDataminingwhichbeingfocusedonwidelybyacademicandindustrialfield,itisamethodoffindingusefulknowledgefrommassdataandoneofahotpointintheresearchareao
2、finternationaldatabaseandinformationdecision.However,withtheaugmentofdatasizeandthedistributionofdatalocation,tradionalcomputingmodelcannotsatisfypracticalrequirement.Gridhasthecharacteristicofresourceshareingandcooperativeprocessing,andprovidesexcellentana
3、lysisandcomputingplatformformassiveanddistributeddata.Inthefollowingchapters,basedongridservices,thekeytechnologiesingriddataminingareresearched,whichincludmassdatapartition,functionminingandsoon.Themaincontributionsareshownasfollows:(1)MassDataPartitionfor
4、RoughSetonAttributeReduction(OAR-RSBSA)whichisbaseduponexistingalgorithmsisproposed.Theefficiencyofthedivisionalgorithmisimprovedabout70%,andthealgorithmcanmakeagoodapplicationintherelevantaspectsofthedatagrid.(2)Inordertofindquicklyattributionreductionofsa
5、mpledata,OptimumAttributionReductiononRoughSetandBinarySearchAlgorithm(OAR-RSBSA)isproposed.Meanwhile,DistributedFunctionMiningonRoughSet,GEPandBinarySearchinGrid(DFMRSGBS)ispresent,whichcombinesgridservicesandattributionreductiontosolvemergeroflocaldatamod
6、elbyideaoffunctionconsistency.ThesimulationexperimentsresultsshowthatOAR-RSBSAisfasterthantraditionalalgorithmsonsolvingoptimumattributionreduction,andaverageconsumptivetimeofDFMRSGBSislessthanGEPandparallelGEPSA.Withincrementofgridnodes,globalfittingerroro
7、fDFMRSGBSisdecreaseapparently.(3)Service-orientedgriddataminingarchitectureispresent.Onthebasisofthearchitecture,thecontentandrelationshipbetweentheeachfunctionalmoduleisdiscussedindetail.(4)Agriddataminingsystem(GDMS)isdesignedandaccomplishedonEclipse.Thed
8、esignofallthemajorfunctioninGDMSandinplementionofgridportalareintroducedindetail.gridportalofGDMSonEclipse/SWTtechnologyisimplementedsoastobeconvientfor南京邮电大学硕士研究生学位论文ABSTRACTIIIuserstoremoteexecutedis
