外文翻译---不完整测量数据的概念建构 英文版.pdf

unsuspectedrelationshipswhichareofinterestorvaluetothedatabasesowners,ordataminers9.Duetothelargenumberofdimensionalityandthehugevolumeofdata,traditionalstatisticalmethodshavetheirlimitationsindatamining.Tomeetthechallengeofdatamining,articialintelligencebasedhumancomputerinteractivetechniqueshavebeenwidelyusedindatamining3,16.*ConceptualconstructiononincompletesurveydataShouhongWanga,*,HaiWangbaDepartmentofMarketing/BusinessInformationSystems,CharltonCollegeofBusiness,UniversityofMassachusettsDartmouth,285OldWestportRoad,NorthDartmouth,MA02747-2300,USAbDepartmentofComputerScience,UniversityofToronto,Toronto,ON,CanadaM5S3G4Received22March2003；receivedinrevisedform9September2003；accepted20October2003Availableonline26November2003AbstractTherawsurveydatafordataminingareoftenincomplete.Theissuesofmissingdatainknowledgediscoveryareoftenignoredindatamining.Thisarticlepresentstheconceptualfoundationsofdataminingwithincompletesurveydata,andproposesqueryprocessingforknowledgediscoveryandasetofqueryfunctionsfortheconceptualconstructioninsurveydatamining.Throughacase,thispaperdemonstratesthatconceptualconstructiononincompletedatacanbeaccomplishedbyusingarticialintelligencetoolssuchasself-organizingmaps.C2112003ElsevierB.V.Allrightsreserved.Keywords:Incompletesurveydata；Surveydatamining；Conceptualconstruction；Self-organizingmaps；Clusteranalysis；Knowledgediscovery；Queryprocessing1.IntroductionDataminingistheprocessoftrawlingthroughdatainthehopeofidentifyinginterpretablepatterns.D/locate/datakData&KnowledgeEngineering49(2004)311323Correspondingauthor.E-mailaddresses:(S.Wang),(H.Wang).0169-023X/$-seefrontmatterC2112003ElsevierB.V.Allrightsreserved.doi:10.1016/j.datak.2003.10.007aneectivemethodindealingwithhigh-dimensionaldata6,12.Moreimportantly,theSOMmethodprovidesabaseforthevisibilityofclustersofhigh-dimensionaldata.Thisfeatureisnot312S.Wang,H.Wang/Data&KnowledgeEngineering49(2004)311323availableinanyotherdataanalysismethods.Itallowsthedataminertoanalyzeclustersbasedontheproblemdomain.Surveyisoneofthecommondataacquisitionmethodsfordatamining4.Indatamining,onecanrarelyndasurveydatasetthatcontainscompleteentriesofeachobservationforallofthevariables.Commonly,surveysandquestionnairesareoftenonlypartiallycompletedbyrespon-dents.Theextentofdamageofmissingdataisunknownwhenitisvirtuallyimpossibletoreturnthesurveyorquestionnairestothedatasourceforcompletion,butisoneofthemostimportantpartsofknowledgefordataminingtodiscover.Infact,missingdataisanimportantdebatableissueintheknowledgeengineeringeld15.Inminingasurveydatabasewithincompletedatathroughclusteranalysis,patternsofthemissingdataaswellasthepotentialimpactsofthesemissingdataontheminingresultsareknowledge.Forinstance,adatamineroftenwishestoknowhowreliableaclusteranalysisis；whenandwhycertaintypesofvaluesareoftenmissing；whatvariablesarecorrelatedintermsofhavingmissingvaluesatthesametime.Thesevaluablepiecesofknowledgecanbediscoveredonlyafterthemissingpartofthedatasetisfullyexplored.Thispaperdiscussestheissueofmissingdatainminingsurveydatabasesforknowledgedis-covery,presentstheconceptualfoundationsofconceptualconstruction,andproposesasetofqueryfunctionsforconceptualconstructioninSOM-baseddatamining.Therestofthepaperisorganizedasfollows.Section2discussestheissuesofmissingdatarelatedtodatamining.Section3introducesSOMforconceptualconstructiononincompletedata.Section4suggestsfourconceptsasknowledgediscoveryindataminingwithincompletedata.ItprovidesaschemeofconceptualconstructiononincompletedatausingSOM.Section5proposesaquerytoolthatisusedtomanipulateSOMforconceptualconstruction.Section6presentsacasestudythatappliesthequerytooltomanipulatetheSOMfortheconceptualconstructiononastudentopinionsurveydataset.Finally,Section7oersconcludingremarks.2.IssuesofmissingdataIncompletedatasetsareubiquitousindatamining.Therehavebeenmanytreatmentsofmissingdata.Oneoftheconvenientsolutionstoincompletedataistoeliminatefromthedatasetthoserecordsthataremissingvalues.This,however,ignorespotentiallyusefulinformationinthoserecords.Incaseswheretheproportionofmissingdataislarge,theconclusionsdrawnfromthescreeneddatasetaremorelikelybiasedormisleading.Therehavebeenmanynon-statisticaltechniquesfordatamining.Theself-organizingmaps(SOM)methodbasedonKohonenneuralnetwork12isoneofthepromisingtechniques.SOM-basedclustertechniqueshaveadvantagesoverothermethodsfordatamining.Dataminingtypicallydealswithveryhigh-dimensionaldata.Thatis,anobservationinthedatabasefordataminingistypicallydescribedbyalargenumberofvariables.Thecurseofdimensionalityturnsstatisticalcorrelationsofdatainsignicant,andthusmakesstatisticalmethodspowerless.TheSOMmethod,however,doesnotrelyonanyassumptionsofstatisticaltests,andisconsideredasS.Wang,H.Wang/Data&KnowledgeEngineering49(2004)311323313Anothersimpleapproachofdealingwithmissingdataistousegenericunknownforallmissingdataitems.Indatamining,unspeciedunknownforallmissingdataitemsoftencausesconfusionandmisinterpretation.Thethirdsolutiontodealingwithmissingdataistoestimatethemissingvalueinthedataeld.Inthecaseoftimeseriesdata,interpolationbasedontwoadjacentdatapointsthatareobservedispossible.Ingeneralcases,onemayusesomeexpectedvalueinthedataeldbasedonstatisticalmeasures7.However,indatamining,surveydataarecommonlyofthetypesofranking,cat-egory,multiplechoices,andbinary.Interpolationanduseofanexpectedvalueforaparticularmissingdatavariableinthesecasesaregenerallyinadequate.Moreimportantly,research2indicatesthatameaningfultreatmentofmissingdatashallalwaysbeindependentoftheproblembeinginvestigated.Morerecently,therehavebeenmathematicalmethodsforndingtheaggregateconceptualdirectionsofadatasetwithmissingdata(e.g.,1,10).Thesemethodsmakethemselvesdistinctfromthetraditionalapproachesoftreatingmissingdatabyfocusingonthecollectiveeectsofthemissingdatainsteadofindividualmissingvalues.Thissuperiorfeatureofthesemethodscanbebestbuiltupfordataminingonincompletedata.However,thesestatisticalmethodshavelimi-tations.First,itisassumedthatmissingvaluesoccurinarandomfashionorfollowacertaindistributionfunctions.Theirstrongassumptionsaboutthedistributionsofdataareofteninvalidespeciallyforcasesofsurveywithincompletedata.Second,thesemathematicalmodelsaredata-driven,insteadofproblem-domain-driven.Infact,asinglegenericconceptualconstructionalgorithmisinsucienttohandleavarietyofgoalsofdataminingsinceagoalofdataminingisoftenrelatedtoitsspecicproblemdomain.Knowledgediscoveryindatabasesisthenon-trivialprocessofidentifyingvalid,novel,potentiallyuseful,andultimatelyunderstandablepatternsofdata8.Followingthisdenition,thisresearchemphasizestwoaspectsofconceptconstructionindataminingwithincompletedata.First,thecriteriaofvalidity,novelty,usefulnessoftheconceptstobeconstructedindataminingwithincompletedatacouldbeproblem-dependent.Thatis,theinterestofadatapatterndependsonthedatamineranddoesnotsolelydependontheestimatedstatisticalstrengthofthepattern14.Second,theconceptualconstructionbasedontheincompletedataisaccomplishedthroughheuristicsearchincombinatorialspacesbuiltoncomputerandhumancognitivetheories13.Humancomputercollaborationconceptconstructionistheinteractiveprocessbetweenthedataminerandcomputertoextractnovel,plausible,useful,relevant,andinterestingknowledgeassociatedwiththemissingdata.Inourview,dataminingdiersfromtraditionalstatisticsindealingmissingdatainmanyways.(1)Dataminingattemptstoextractunsuspectedandpotentiallyusefulpatternsfromthedataforthedataminerswithnovelgoalsrelatedtothemissingdata,ratherthantoestimatetheindi-vidualvaluesofthemissingdata.(2)Dataminingisahumancenteredprocessimplementedthroughknowledgediscoveryloopscoupledwithhumancomputerinteractiontoperceivetheimpactofthemissingdataatanaggregatelevel,ratherthanaone-waymathematicalderivationbasedonunveriedassump-tions.3.Toolforconceptualconstruction:self-organizingmaps(SOM)Givenalargesetofhigh-dimensionalsurveysamples,thereusuallybeasignicantnumberofobservationshavemissingvalues；however,notallmissingdataarerelevanttothedataminerC213sinterest.Hence,anysimplebrute-forcesearchmethodformissingdataisnotonlyinfeasibleforahugeamountofdata,butalsohelplesswhenthedatamineristoidentifyproblems,ordevelopconcepts,throughdatamining.Toidentifyproblemsordevelopconcepts,thedataminerneedsatooltoobserveunsuspectedpatternsoftheavailabledataandthemissingparts.Self-organizingmaps(SOM)12havebeenwidelyusedforclustering,sinceSOMaremorecomputationallyecientthanthepopulark-meansclusteringalgorithm.Moreimportantly,SOMprovidedatavisualizationforthedataminertoviewhigh-dimensionaldata11.Research14,16314S.Wang,H.Wang/Data&KnowledgeEngineering49(2004)311323indicatesthatSOMareeectiveindataminingfortheidenticationofunsuspectedpatternofthedata.Specically,SOMcanbeusedforclusteranalysisonmultivariatesurveydata.ThisstudytakesonestepfurtherandusesSOMasatoolforconceptconstructionrelatedtomissingdata.Conceptualconstructiononincompletedataistoinvestigatethepatternsofthemissingdataaswellasthepotentialimpactsofthesemissingdataontheminingresultsbasedonlyonthecompletedata.Asseenlaterinourillustrativeexamples,SOMprovideamechanismforhumancomputercollaborationtoconstructconceptsfromthedatawithmissingvalues.SOMcanlearncertainusefulfeaturesfo