数据挖掘外文翻译参考文献Word文档下载推荐.docx

数据挖掘外文翻译参考文献Word文档下载推荐.docx

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Simplystated,dataminingreferstoextractingor“mining”knowledgefromlargeamountsofdata.Thetermisactuallyamisnomer.Rememberthattheminingofgoldfromrocksorsandisreferredtoasgoldminingratherthanrockorsandmining.Thus,“datamining”shouldhavebeenmoreappropriatelynamed“knowledgeminingfromdata”,whichisunfortunatelysomewhatlong.“Knowledgemining”,ashorterterm,maynotreflecttheemphasisonminingfromlargeamountsofdata.Nevertheless,miningisavividtermcharacterizingtheprocessthatfindsasmallsetofpreciousnuggetsfromagreatdealofrawmaterial.Thus,suchamisnomerwhichcarriesboth“data”and“mining”becameapopularchoice.Therearemanyothertermscarryingasimilarorslightlydifferentmeaningtodatamining,suchasknowledgeminingfromdatabases,knowledgeextraction,data/patternanalysis,dataarchaeology,anddatadredging.

Manypeopletreatdataminingasasynonymforanotherpopularlyusedterm,“KnowledgeDiscoveryinDatabases”,orKDD.Alternatively,othersviewdataminingassimplyanessentialstepintheprocessofknowledgediscoveryindatabases.Knowledgediscoveryconsistsofaniterativesequenceofthefollowingsteps:

·

datacleaning:

toremovenoiseorirrelevantdata,

dataintegration:

wheremultipledatasourcesmaybecombined,

dataselection:

wheredatarelevanttotheanalysistaskareretrievedfromthedatabase,

datatransformation:

wheredataaretransformedorconsolidatedintoformsappropriateforminingbyperformingsummaryoraggregationoperations,forinstance,

datamining:

anessentialprocesswhereintelligentmethodsareappliedinordertoextractdatapatterns,

patternevaluation:

toidentifythetrulyinterestingpatternsrepresentingknowledgebasedonsomeinterestingnessmeasures,and

knowledgepresentation:

wherevisualizationandknowledgerepresentationtechniquesareusedtopresenttheminedknowledgetotheuser.

Thedataminingstepmayinteractwiththeuseroraknowledgebase.Theinterestingpatternsarepresentedtotheuser,andmaybestoredasnewknowledgeintheknowledgebase.Notethataccordingtothisview,dataminingisonlyonestepintheentireprocess,albeitanessentialonesinceituncovershiddenpatternsforevaluation.

Weagreethatdataminingisaknowledgediscoveryprocess.However,inindustry,inmedia,andinthedatabaseresearchmilieu,theterm“datamining”isbecomingmorepopularthanthelongertermof“knowledgediscoveryindatabases”.Therefore,inthisbook,wechoosetousetheterm“datamining”.Weadoptabroadviewofdataminingfunctionality:

dataminingistheprocessofdiscoveringinterestingknowledgefromlargeamountsofdatastoredeitherindatabases,datawarehouses,orotherinformationrepositories.

Basedonthisview,thearchitectureofatypicaldataminingsystemmayhavethefollowingmajorcomponents:

1.Database,datawarehouse,orotherinformationrepository.Thisisoneorasetofdatabases,datawarehouses,spreadsheets,orotherkindsofinformationrepositories.Datacleaninganddataintegrationtechniquesmaybeperformedonthedata.

2.Databaseordatawarehouseserver.Thedatabaseordatawarehouseserverisresponsibleforfetchingtherelevantdata,basedontheuser’sdataminingrequest.

3.Knowledgebase.Thisisthedomainknowledgethatisusedtoguidethesearch,orevaluatetheinterestingnessofresultingpatterns.Suchknowledgecanincludeconcepthierarchies,usedtoorganizeattributesorattributevaluesintodifferentlevelsofabstraction.Knowledgesuchasuserbeliefs,whichcanbeusedtoassessapattern’sinterestingnessbasedonitsunexpectedness,mayalsobeincluded.Otherexamplesofdomainknowledgeareadditionalinterestingnessconstraintsorthresholds,andmetadata（e.g.,describingdatafrommultipleheterogeneoussources）.

4.Dataminingengine.Thisisessentialtothedataminingsystemandideallyconsistsofasetoffunctionalmodulesfortaskssuchascharacterization,associationanalysis,classification,evolutionanddeviationanalysis.

5.Patternevaluationmodule.Thiscomponenttypicallyemploysinterestingnessmeasuresandinteractswiththedataminingmodulessoastofocusthesearchtowardsinterestingpatterns.Itmayaccessinterestingnessthresholdsstoredintheknowledgebase.Alternatively,thepatternevaluationmodulemaybeintegratedwiththeminingmodule,dependingontheimplementationofthedataminingmethodused.Forefficientdatamining,itishighlyrecommendedtopushtheevaluationofpatterninterestingnessasdeepaspossibleintotheminingprocesssoastoconfinethesearchtoonlytheinterestingpatterns.

6.Graphicaluserinterface.Thismodulecommunicatesbetweenusersandthedataminingsystem,allowingtheusertointeractwiththesystembyspecifyingadataminingqueryortask,providinginformationtohelpfocusthesearch,andperformingexploratorydataminingbasedontheintermediatedataminingresults.Inaddition,thiscomponentallowstheusertobrowsedatabaseanddatawarehouseschemasordatastructures,evaluateminedpatterns,andvisualizethepatternsindifferentforms.

Fromadatawarehouseperspective,dataminingcanbeviewedasanadvancedstageofon-1ineanalyticalprocessing（OLAP）.However,datamininggoesfarbeyondthenarrowscopeofsummarization-styleanalyticalprocessingofdatawarehousesystemsbyincorporatingmoreadvancedtechniquesfordataunderstanding.

Whiletheremaybemany“dataminingsystems”onthemarket,notallofthemcanperformtruedatamining.Adataanalysissystemthatdoesnothandlelargeamountsofdatacanatmostbecategorizedasamachinelearningsystem,astatisticaldataanalysistool,oranexperimentalsystemprototype.Asystemthatcanonlyperformdataorinformationretrieval,includingfindingaggregatevalues,orthatperformsdeductivequeryansweringinlargedatabasesshouldbemoreappropriatelycategorizedaseitheradatabasesystem,aninformationretrievalsystem,oradeductivedatabasesystem.

Datamininginvolvesanintegrationoftechniquesfrommult1pledisciplinessuchasdatabasetechnology,statistics,machinelearning,highperformancecomputing,patternrecognition,neuralnetworks,datavisualization,informationretrieval,imageandsignalprocessing,andspatialdataanalysis.Weadoptadatabaseperspectiveinourpresentationofdatamininginthisbook.Thatis,emphasisisplacedonefficientandscalabledataminingtechniquesforlargedatabases.Byperformingdatamining,interestingknowledge,regularities,orhigh-levelinformationcanbeextractedfromdatabasesandviewedorbrowsedfromdifferentangles.Thediscoveredknowledgecanbeappliedtodecisionmaking,processcontrol,informationmanagement,queryprocessing,andsoon.Therefore,dataminingisconsideredasoneofthemostimportantfrontiersindatabasesystemsandoneofthemostpromising,newdatabaseapplicationsintheinformationindustry.

Aclassificationofdataminingsystems

Dataminingisaninterdisciplinaryfield,theconfluenceofasetofdisciplines,includingdatabasesystems,statistics,machinelearning,visualization,andinformationscience.Moreover,dependingonthedataminingapproachused,techniquesfromotherdisciplinesmaybeapplied,suchasneuralnetworks,fuzzyandorroughsettheory,knowledgerepresentation,inductivelogicprogramming,orhighperformancecomputing.Dependingonthekindsofdatatobeminedoronthegivendataminingapplication,thedataminingsystemmayalsointegratetechniquesfromspatialdataanalysis,Informationretrieval,patternrecognition,imageanalysis,signalprocessing,computergraphics,Webtechnology,economics,orpsychology.

Becauseofthediversityofdisciplinescontributingtodatamining,dataminingresearchisexpectedtogeneratealargevarietyofdataminingsystems.Therefore,itisnecessarytoprovideaclearclassificationofdataminingsystems.Suchaclassificationmayhelppotentialusersdistinguishdataminingsystemsandidentifythosethatbestmatchtheirneeds.Dataminingsystemscanbecategorizedaccordingtovariouscriteria,asfollows.

1）Classificationaccordingtothekindsofdatabasesmined.

Adataminingsystemcanbeclassifiedaccordingtothekindsofdatabasesmined.Databasesystemsthemselvescanbeclassifiedaccordingtodifferentcriteria（suchasdatamodels,orthetypesofdataorapplicationsinvolved）,eachofwhichmayrequireitsowndataminingtechnique.Dataminingsystemscanthereforebeclassifiedaccordingly.

Forinstance,ifclassifyingaccordingtodatamodels,wemayhavearelational,transactional,object-oriented,object-relational,ordatawarehouseminingsystem.Ifclassifyingaccordingtothespecialtypesofdatahandled,wemayhaveaspatial,time-series,text,ormultimediadataminingsystem,oraWorld-WideWebminingsystem.Othersystemtypesincludeheterogeneousdataminingsystems,andlegacydataminingsystems.

2）Classificationaccordingtothekindsofknowledgemined.

Dataminingsystemscanbecategorizedaccordingtothekindsofknowledgetheymine,i.e.,basedondataminingfunctionalities,suchascharacterization,discrimination,association,classification,clustering,trendandevolutionanalysis,deviationanalysis,similarityanalysis,etc.Acomprehensivedataminingsystemusuallyprovidesmultipleand/orintegrateddataminingfunctionalities.

Moreover,dataminingsystemscanalsobedistinguishedbasedonthegranularityorlevelsofabstractionoftheknowledgemined,includinggeneralizedknowledge（atahigh