翻译原文-不完整测量数据的概念建构.pdf

UNSUSPECTEDRELATIONSHIPSWHICHAREOFINTERESTORVALUETOTHEDATABASESOWNERS,ORDATAMINERS9DUETOTHELARGENUMBEROFDIMENSIONALITYANDTHEHUGEVOLUMEOFDATA,TRADITIONALSTATISTICALMETHODSHAVETHEIRLIMITATIONSINDATAMININGTOMEETTHECHALLENGEOFDATAMINING,ARTICIALINTELLIGENCEBASEDHUMANCOMPUTERINTERACTIVETECHNIQUESHAVEBEENWIDELYUSEDINDATAMINING3,16CONCEPTUALCONSTRUCTIONONINCOMPLETESURVEYDATASHOUHONGWANGA,HAIWANGBADEPARTMENTOFMARKETING/BUSINESSINFORMATIONSYSTEMS,CHARLTONCOLLEGEOFBUSINESS,UNIVERSITYOFMASSACHUSETTSDARTMOUTH,285OLDWESTPORTROAD,NORTHDARTMOUTH,MA027472300,USABDEPARTMENTOFCOMPUTERSCIENCE,UNIVERSITYOFTORONTO,TORONTO,ON,CANADAM5S3G4RECEIVED22MARCH2003RECEIVEDINREVISEDFORM9SEPTEMBER2003ACCEPTED20OCTOBER2003AVAILABLEONLINE26NOVEMBER2003ABSTRACTTHERAWSURVEYDATAFORDATAMININGAREOFTENINCOMPLETETHEISSUESOFMISSINGDATAINKNOWLEDGEDISCOVERYAREOFTENIGNOREDINDATAMININGTHISARTICLEPRESENTSTHECONCEPTUALFOUNDATIONSOFDATAMININGWITHINCOMPLETESURVEYDATA,ANDPROPOSESQUERYPROCESSINGFORKNOWLEDGEDISCOVERYANDASETOFQUERYFUNCTIONSFORTHECONCEPTUALCONSTRUCTIONINSURVEYDATAMININGTHROUGHACASE,THISPAPERDEMONSTRATESTHATCONCEPTUALCONSTRUCTIONONINCOMPLETEDATACANBEACCOMPLISHEDBYUSINGARTICIALINTELLIGENCETOOLSSUCHASSELFORGANIZINGMAPSC2112003ELSEVIERBVALLRIGHTSRESERVEDKEYWORDSINCOMPLETESURVEYDATASURVEYDATAMININGCONCEPTUALCONSTRUCTIONSELFORGANIZINGMAPSCLUSTERANALYSISKNOWLEDGEDISCOVERYQUERYPROCESSING1INTRODUCTIONDATAMININGISTHEPROCESSOFTRAWLINGTHROUGHDATAINTHEHOPEOFIDENTIFYINGINTERPRETABLEPATTERNSDATAMININGISDIERENTFROMTRADITIONALSTATISTICALANALYSISINTHATITISAIMEDATNDINGWWWELSEVIERCOM/LOCATE/DATAKDATAWHENANDWHYCERTAINTYPESOFVALUESAREOFTENMISSINGWHATVARIABLESARECORRELATEDINTERMSOFHAVINGMISSINGVALUESATTHESAMETIMETHESEVALUABLEPIECESOFKNOWLEDGECANBEDISCOVEREDONLYAFTERTHEMISSINGPARTOFTHEDATASETISFULLYEXPLOREDTHISPAPERDISCUSSESTHEISSUEOFMISSINGDATAINMININGSURVEYDATABASESFORKNOWLEDGEDISCOVERY,PRESENTSTHECONCEPTUALFOUNDATIONSOFCONCEPTUALCONSTRUCTION,ANDPROPOSESASETOFQUERYFUNCTIONSFORCONCEPTUALCONSTRUCTIONINSOMBASEDDATAMININGTHERESTOFTHEPAPERISORGANIZEDASFOLLOWSSECTION2DISCUSSESTHEISSUESOFMISSINGDATARELATEDTODATAMININGSECTION3INTRODUCESSOMFORCONCEPTUALCONSTRUCTIONONINCOMPLETEDATASECTION4SUGGESTSFOURCONCEPTSASKNOWLEDGEDISCOVERYINDATAMININGWITHINCOMPLETEDATAITPROVIDESASCHEMEOFCONCEPTUALCONSTRUCTIONONINCOMPLETEDATAUSINGSOMSECTION5PROPOSESAQUERYTOOLTHATISUSEDTOMANIPULATESOMFORCONCEPTUALCONSTRUCTIONSECTION6PRESENTSACASESTUDYTHATAPPLIESTHEQUERYTOOLTOMANIPULATETHESOMFORTHECONCEPTUALCONSTRUCTIONONASTUDENTOPINIONSURVEYDATASETFINALLY,SECTION7OERSCONCLUDINGREMARKS2ISSUESOFMISSINGDATAINCOMPLETEDATASETSAREUBIQUITOUSINDATAMININGTHEREHAVEBEENMANYTREATMENTSOFMISSINGDATAONEOFTHECONVENIENTSOLUTIONSTOINCOMPLETEDATAISTOELIMINATEFROMTHEDATASETTHOSERECORDSTHATAREMISSINGVALUESTHIS,HOWEVER,IGNORESPOTENTIALLYUSEFULINFORMATIONINTHOSERECORDSINCASESWHERETHEPROPORTIONOFMISSINGDATAISLARGE,THECONCLUSIONSDRAWNFROMTHESCREENEDDATASETAREMORELIKELYBIASEDORMISLEADINGTHEREHAVEBEENMANYNONSTATISTICALTECHNIQUESFORDATAMININGTHESELFORGANIZINGMAPSSOMMETHODBASEDONKOHONENNEURALNETWORK12ISONEOFTHEPROMISINGTECHNIQUESSOMBASEDCLUSTERTECHNIQUESHAVEADVANTAGESOVEROTHERMETHODSFORDATAMININGDATAMININGTYPICALLYDEALSWITHVERYHIGHDIMENSIONALDATATHATIS,ANOBSERVATIONINTHEDATABASEFORDATAMININGISTYPICALLYDESCRIBEDBYALARGENUMBEROFVARIABLESTHECURSEOFDIMENSIONALITYTURNSSTATISTICALCORRELATIONSOFDATAINSIGNICANT,ANDTHUSMAKESSTATISTICALMETHODSPOWERLESSTHESOMMETHOD,HOWEVER,DOESNOTRELYONANYASSUMPTIONSOFSTATISTICALTESTS,ANDISCONSIDEREDASSWANG,HWANG/DATAHOWEVER,NOTALLMISSINGDATAARERELEVANTTOTHEDATAMINERC213SINTERESTHENCE,ANYSIMPLEBRUTEFORCESEARCHMETHODFORMISSINGDATAISNOTONLYINFEASIBLEFORAHUGEAMOUNTOFDATA,BUTALSOHELPLESSWHENTHEDATAMINERISTOIDENTIFYPROBLEMS,ORDEVELOPCONCEPTS,THROUGHDATAMININGTOIDENTIFYPROBLEMSORDEVELOPCONCEPTS,THEDATAMINERNEEDSATOOLTOOBSERVEUNSUSPECTEDPATTERNSOFTHEAVAILABLEDATAANDTHEMISSINGPARTSSELFORGANIZINGMAPSSOM12HAVEBEENWIDELYUSEDFORCLUSTERING,SINCESOMAREMORECOMPUTATIONALLYECIENTTHANTHEPOPULARKMEANSCLUSTERINGALGORITHMMOREIMPORTANTLY,SOMPROVIDEDATAVISUALIZATIONFORTHEDATAMINERTOVIEWHIGHDIMENSIONALDATA11RESEARCH14,16314SWANG,HWANG/DATAJVMIWHEREVMIJISTHENUMBEROFMISSINGVALUESINBOTHVARIABLESIANDJ,ANDVMIISTHENUMBEROFMISSINGVALUESINVARIABLEITHISCONCEPTDISCLOSESTHECORRELATIONOFTWOVARIABLESINTERMSOFMISSINGVALUESTHEHIGHERTHEVALUEVMIJVMIIS,THESTRONGERTHECORRELATIONOFMISSINGVALUESWOULDBE44CONDITIONALEECTSTHECONCEPTOFCONDITIONALEECTSREVEALSTHEPOTENTIALCHANGESOFTHECLUSTERSIDENTIEDIFTHEMISSINGVALUESHADCOMPLETEDC5DPJ8ZIKWHEREDPISTHECHANGESOFTHECLUSTERSPERCEIVEDBYTHEDATAMINER,8ZIREPRESENTSALLMISSINGVALUESOFVARIABLEI,ANDKISTHEPOSSIBLEVALUEVARIABLEIMIGHTHAVEFORTHESURVEYTYPICALLY,KFMAXMINPGWHEREMAXISTHEMAXIMALVALUEOFTHESCALE,MINISTHEMINIMALVALUEOFTHESCALE,ANDPISTHERANDOMVARIABLEWITHTHESAMEDISTRIBUTIONFUNCTIONOFTHEVALUESINTHECOMPLETEDATABYSETTINGDIERENTPOSSIBLEVALUESOFKFORTHEMISSINGVALUES,THEDATAMINERISABLETOOBSERVETHECHANGESOFCLUSTERSANDREDENETHEPROBLEMINSUMMARY,CONCEPTUALCONSTRUCTIONONINCOMPLETEDATAISAKNOWLEDGEDEVELOPMENTPROCESSTOCONSTRUCTNEWCONCEPTSONINCOMPLETEDATA,THEDATAMINERNEEDSTOIDENTIFYAPARTICULARPROBLEMASABASEFORTHECONSTRUCTIONFOURCONCEPTSONMISSINGDATAARERELIABILITY,HIDING,COMPLEMENTING,ANDCONDITIONALEECTSNEXT,WEDEVELOPASETOFQUERIESFORCONCEPTUALCONSTRUCTIONONINCOMPLETEDATAOUROBJECTIVEOFTHESEQUERIESISTOALLOWTHEDATAMINERTOCONDUCTTHEEXPERIMENTALPROCESSTHROUGHTHEUSEOFSOMINORDERTOCONSTRUCTNEWCONCEPTSRELATEDTOTHEPROBLEM5QUERYPROCESSINGFORCONCEPTUALCONSTRUCTIONQUERYTOOLSARECHARACTERIZEDBYSTRUCTUREDQUERYLANGUAGESQL,THESTANDARDQUERYLANGUAGEFORRELATIONALDATABASEMANAGEMENTSYSTEMSFORDATAMINING,ASTHEULTIMATEOBJECTIVEOFINFORMATIONRETRIEVALFROMTHEDATABASEISTHEFORMULATIONOFKNOWLEDGETHROUGHTHEUSEOFAVARIETYOFTECHNIQUES,ITISUNLIKELYTHATASINGLESTANDARDQUERYLANGUAGECANBECREATEDFORALLPURPOSESOFDATAMININGNEVERTHELESS,TOSUPPORTHUMANCOMPUTERCOLLABORATIONEECTIVELY,VISUALIZEDQUERYPROCESSINGISNECESSARYINDATAMINING5THISSTUDYDEVELOPSASETOFQUERYFUNCTIONSTHATASSISTTHEDATAMINERTOCONSTRUCTCONCEPTSRELATEDTOTHEMISSINGDATATHROUGHTHEC3VMIJXJABWHEREVMIISTHENUMBEROFMISSINGVALUESINVARIABLEI,XJISTHEVALUEOFVARIABLEJ,ANDABISTHERANGEOFVALUESOFXJTHISINDEXDISCLOSESTHEDEGREEOFUNCERTAINTYOFANSWERSTOTHESURVEYQUESTION,SUCHASDONOTKNOWANDNEUTRAL,ORTHEINTENTIONOFSYSTEMATICALMISSINGDATA,SUCHASDONOTWANTTOTELL43COMPLEMENTINGTHECONCEPTOFCOMPLEMENTINGREVEALSWHATVARIABLESAREMORELIKELYTOHAVEMISSINGVALUESAT316SWANG,HWANG/DATAJVMI57FINDC5DPJ8ZIKTHISQUERYFUNCTIONALLOWSTHEDATAMINERTOREPLACETHEMISSINGVALUESWITHHYPOTHETICALVALUES,ANDOBSERVETHECHANGESOFTHECLUSTERSTHEHYPOTHETICALVALUESCOULDBEANYBETWEENTHEPOSSIBLEMAXIMALANDMINIMALVALUESINSTEADOFRETURNINGASPECICNUMBER,THISQUERYFUNCTIONRETURNSVARIOUSMAPSFORTHEDATAMINERTOCOMPARETHECLUSTERSUSINGDIERENTKVALUESBASEDONWHATIFTRIALS,THEDATAMINERISABLETOPERCEIVETHEIMPACTSOFTHEMISSINGVALUESONTHETHISQUERYFUNCTIONALLOWSTHEDATAMINERTONDTHECORRELATIONOFTHEMISSINGVALUESINTWOVARIABLESUSINGTHISQUERY,THEDATAMINERRSTCHOOSESTWOVARIABLESTOBEINVESTIGATEDTHATARERELEVANTTOTHEPROBLEM,ANDTHENNDSOUTHOWOFTENTHETWOVARIABLESMIGHTHAVEMISSINGVALUESTOGETHERTHETWOVARIABLESTHATMIGHTHAVECORRELATIONOFMISSINGVALUESANDTHERANGEOFTHEKNOWNVALUESINONEVARIABLE,ANDRECEIVESTHENUMBEROFOBSERVATIONSWITHMISSINGVALUESINTHEOTHERVARIABLETHEDATAMINERMIGHTBEINTERESTEDINAPARTICULARVARIABLEUSINGTHISQUERY,THEDATAMINERISALLOWEDTOCHECKWHETHERTHECLUSTERSOBSERVEDARERELIABLEINTERMSOFTHEPARTICULARVARIABLE55FINDC3VMIJXJABTHISQUERYFUNCTIONALLOWSTHEDATAMINERTONDTHECORRELATIONOFTHEMISSINGVALUESINONEVARIABLEANDTHEVALUERANGEINANOTHERVARIABLETHISCORRELATIONPROVIDESKNOWLEDGEAFTERCHOOSINGVARIABLESANDIDENTIFYINGCLUSTERSTHROUGHSOM,THEDATAMINERWOULDLIKETOKNOWHOWRELIABLEOFTHEOBSERVEDCLUSTERSARETHISQUERYALLOWSTHEDATAMINERTONDSMSCINTHEVARIABLESUSEDFORTHESOMTRAININGIFTHEVALUESMSCISHIGH,THEDATAMINERCANNDTHERELIABILITYFORINDIVIDUALVARIABLES,ASDESCRIBEDNEXT52SAVEANDRETRIEVETHESOMTHISQUERYISAGENERALOPERATIONFORSAVINGANDRETRIEVINGTHESOMALONGWITHALLTHESETTINGFORPARAMETERS,VARIABLESOFTHEDATASAMPLESTHEDATAMINERISALLOWEDTOCOMPAREANUMBEROFSOMRESULTSTOCONSTRUCTCONCEPTSONINCOMPLETEDATA318SWANG,HWANG/DATASPECICALLY,THETEXTBOOKSDONOTGIVEMUCHHELPALTHOUGHV20HASTHEHIGHESTRATEOFMISSINGVALUES,MISSINGVALUESDONOTHAVEASIGNICANTIMPACTONTHEPROBLEMIDENTIEDINTHISCASE,V20HADTHEHIGHESTRATEOFMISSINGVALUES86THEDATAMINERWOULDLIKETOSEEPROBLEMIDENTIED320SWANG,HWANG/DATA016JV202021JV203028JV204024JV205GFORTHECOMPLETEDATAAFTERSETTINGTHESEVALUESFORTHEMISSINGVALUESOFV20,NEWTRIALDATAWEREUSEDFORSOMTOGENERATEMAPSUSINGTHESAMETOPOLOGYOFTHESOMFORTHECOMPLETEDATA,THEWHATIFTRIALSWERECONDUCTEDASSHOWNINFIG3,THEOVERALLCONCLUSIONINTHISCASEWASTHATTHEMISSINGVALUESINV20DIDNOTHAVESIGNICANTIMPACTONTHEANALYSIS,VARIABLESV1,V14,V16,V18,ANDV20THATRECEIVEDLOWVALUESWEREFOUNDTOBEPARTICULARLYRELEVANTTOTHEPROBLEMOFINEECTIVETEACHING,ASSUMMARIZEDINTHERSTTHREECOLUMNSOFTABLE1INCOMPLETEDATAWERETHENAPPLIEDTOCONSTRUCTNEWCONCEPTSOFTHEPROBLEM61C1SMSCALTHOUGHTHERATEOFINCOMPLETEOBSERVATIONSFORTHEENTIRESURVEYWASASHIGHAS37,SMSCINTERMSOFTHEVERYRELEVANTVARIABLESV1,V14,V16,V18,ANDV20WAS52,INDICATINGTHATTHEPROBLEMINITIALLYIDENTIEDWASGENERALLYVALID62C2VMIVCIAMONGTHEVEVARIABLES,THERATEOFMISSINGVALUESINV20WASTHEHIGHESTAT86,INDICATINGTHATTHEDEPENDABILITYOFINEECTIVETEACHINGONTHISVARIABLEIE,THEUSEFULNESSOFTHETEXTBOOKANDTEACHINGMATERIALMIGHTNOTBEASRELIABLEASOTHERVERYRELEVANTVARIABLES63C3VMIJXJABTHERATEOFMISSINGVALUESINV16WAS22HOWEVER,521OFTHEMISSINGVALUESCAMEFROMTHEOBSERVATIONSWITHXV151,3THISINDICATEDTHATSTUDENTSWHOWERENOTSATISEDWITHPROMPTGRADINGOFTENDISREGARDEDWHETHERTHEYRECEIVEDHELPFULCOMMENTSFORTHEIRWORK64C4VMIJVMITHERATEOFMISSINGVALUESOFV14WAS37HOWEVER,THEMISSINGVALUESOFV14ANDV10WEREHIGHLYCORRELATEDWITHVMV14V10VMV14336THISINDICATEDTHATSTUDENTSWHOOMITTEDTHEOPINIONONTHELEARNINGEXPERIENCEFROMTHECOURSEOFTENDISREGARDEDWHETHERTHETESTSORASSIGNMENTSWEREAPPROPRIATELYDESIGNED65C5DPJ8ZIKANEXPERIMENTALTESTOFTHEPROPOSEDMETHODCLEARLY,THESCALEOFDATAMININGINTHISCASESTUDYISCEPTUALCONSTRUCTIONONINCOMPLETEDATAUSINGTHESEQUERYFUNCTIONS,THEDATAMINERISALLOWEDTOCONSTRUCTNEWCONCEPTSRELATEDTOTHEPROBLEMIDENTIEDFORDATAMININGTHROUGHTHEREALWORLDCASE,ITHASBEENDEMONSTRATEDTHATTHEMODELOFCONCEPTUALCONSTRUCTIONCANBETTERBEUSEDFORKNOWLEDGEDISCOVERYRATHERSMALLINGENERAL,DATAMININGISAPPLIEDONMUCHLARGERDATASETSTHANTHISEXAMPLEINTHEASPECTSOFSAMPLESIZEANDDIMENSIONALITY7CONCLUSIONSINTHEDATAMININGELD,INCOMPLETEDATAAREOFTENMISTREATEDTHISSTUDYPROPOSESCONCEPTUALCONSTRUCTIONONINCOMPLETEDATAFOURCATEGORIESOFCONCEPTSONMISSINGDATAAREPROPOSEDTHEYARERELIABILITYOFTHEPROBLEMIDENTIED,INTENTIONOFDATAHIDING,COMPLEMENTINGOFMISSINGVALUESINTWOVARIABLES,ANDCONDITIONALEECTSOFTHEMISSINGDATASOMARESELECTEDASTHETOOLFORCONCEPTUALCONSTRUCTIONDUETOTHEIRADVANTAGESINCLUSTERINGANDDATAVISUALIZATIONBUILDINGONSOMCLUSTERINGANALYSIS,THISPAPERTHENSUGGESTSSEVENCATEGORIESOFQUERYFUNCTIONSFORCONFIG3EXAMPLESOFSOMFORWHATIFTRIALSSWANG,HWANG/DATAKNOWLEDGEENGINEERING492004311323321KNOWLEDGEDISCOVERYINDATABASESISAGROWINGELDGENERALLY,KNOWLEDGEDISCOVERYSTARTSWITHTHEORIGINALPROBLEMIDENTICATIONYETTHEVALIDATIONOFTHEPROBLEMIDENTIEDISTYPICALLYBEYONDTHEDATABASEANDGENERICSTATISTICALALGORITHMSTHEMSELVESDURINGTHEKNOWLEDGEDISCOVERYPROCESS,NEWCONCEPTSMUSTBECONSTRUCTEDTHROUGHDEMYSTIFYINGTHEDATAINCONCLUSION,CONCEPTUALCONSTRUCTIONONINCOMPLETEDATAPROVIDESEECTIVETECHNIQUESFORKNOWLEDGEDEVELOPMENTSOTHATTHEDATAMINERISALLOWEDTOINTERPRETTHEDATAMININGRESULTSBASEDONTHEPARTICULARPROBLEMDOMAINANDHIS/HERPERCEPTIONOFTHEMISSINGDATAFUTUREWORKINCLUDESIMPLEMENTINGTHESOFTWARESYSTEMONAMAINFRAMEBASEDDATABASESYSTEMANDFURTHEREVALUATINGTHEPROPOSEDAPPROACHONDATASETSWITHAMUCHLARGERSCALEACKNOWLEDGEMENTSTHEAUTHORSAREINDEBTEDTOTWOANONYMOUSREFEREESFORTHEIRVALUABLECOMMENTSFORTHEREVISIONOFTHISPAPERV18TESTANDASSIGNMENTSPROVIDEADEQUATEFEEDBACKONSTUDENTPROGRESSV19THEINSTRUCTORISWILLINGTOSCHEDULECONSULTATIONTIMEWITHTHESTUDENTSV20THETEXTBOOKSANDCOURSEMATERIALAREUSEFULV21INCOMPARISONTOOTHERINSTRUCTORS,THISINSTRUCTORISANEECTIVETEACHERREFERENCES1CCAGGARWAL,SPARTHASARATHY,MININGMASSIVELYINCOMPLETEDATASETSBYCONCEPTUALRECONSTRUCTION,INPROCEEDINGSOFTHE7THACMSIGKDDINTERNATIONALCONFERENCEONKNOWLEDGEDISCOVERYANDDATAMINING,ACMPRESS,NEWYORK,2001,PP2272322GBATISTA,MMONARD,ANANALYSISOFFOURMISSINGDATATREATMENTMETHODSFORSUPERVISEDLEARNING,APPLIEDARTICIALINTELLIGENCE175/620035195333RJBRACHMAN,TKHABAZA,WKLOESGEN,GPIATETSKYSHAPIRO,GESIMOUDIS,MININGBUSINESSDATABASES,COMMUNICATIONSOFTHEACM3911199642484SBRIN,RRASTOGI,KSHIM,MININGOPTIMIZEDGAINRULESFORNUMERICATTRIBUTES,IEEETRANSACTIONSONKNOWLEDGEANDDATAENGINEERING15220033243385LCHITTARO,CCOMBI,VISUALIZINGQUERIESONDATABASEOFTEMPORALHISTORIESNEWMETAPHORSANDTHEIREVALUATION,DATAANDKNOWLEDGEENGINEERING442020032392646GDEBOECK,TKOHONEN,VISUALEXPLORATIONSINFINANCEWITHSELFORGANIZINGMAPS,SPRINGERVERLAG,LONDON,UK,19987APDEMPSTER,NMLAIRD,DBRUBIN,MAXIMUMLIKELIHOODFROMINCOMPLETEDATAVIATHEEMALGORITHM,JOURNALOFTHEROYALSTATISTICALSOCIETY,SERIESBMETHODOLOGICAL39119771388UFAYYAD,GPIATETSKYSHAPIRO,PSMYTH,THEKDDPROCESSFOREXTRACTINGUSEFULKNOWLEDGEFROMVOLUMESOFDATA,COMMUNICATIONSOFTHEACM3911199627349DJHAND,DATAMININGSTATISTICSANDMORE,THEAMERICANSTATISTICIAN522199811211810ITJOLLIE,PRINCIPLECOMPONENTANALYSIS,SPRINGERVERLAG,NEWYORK,198611DAKEIM,HPKRIEGEL,VISUALIZATIONTECHNIQUESFORMININGLARGEDATABASESACOMPARISON,IEEETRANSACTIONSAPPENDIXATHEQUESTIONNAIREOFSTUDENTOPINIONOFTEACHERSSURVEYV1THEINSTRUCTOREXPLAINSDICULTCONCEPTSCLEARLYANDUNDERSTANDABLYV2CLASSSESSIONSAPPEARTOBECAREFULLYPLANNEDV3THEINSTRUCTORCONVEYSSTRONGINTERESTANDENTHUSIASMV4STUDENTSAREENCOURAGEDTOEXPRESSTHEIRVIEWSANDPARTICIPATEINCLASSV5THEINSTRUCTORSHOWSAGENUINECONCERNFORSTUDENTPROGRESSV6THEINSTRUCTORSTIMULATESSTUDENTSTOTHINKFORTHEMSELVESV7EECTIVEUSEISMADEOFEXAMPLESANDILLUSTRATIONSV8THEINSTRUCTORSPEAKSINAWAYWHICHCANBECLEARLYUNDERSTOODV9THEINSTRUCTORMAKESITCLEARHOWEACHTOPICTSINTOTHECOURSEV10THISCOURSEWASAPOSITIVELEARNINGEXPERIENCEV11CLASSESAREHELDREGULARLYTOANAGREEDSCHEDULEV12THEVARIOUSPARTSOFTHECOURSEAREEECTIVELYCOORDINATEDV13COURSEREQUIREMENTSARECOMMUNICATEDCLEARLYANDEXPLICITLYV14TESTSANDASSIGNMENTSAREREASONABLEMEASURESOFSTUDENTLEARNINGV15WHEREAPPROPRIATE,STUDENTWORKISGRADEDPROMPTLYV16WHEREAPPROPRIATE,HELPFULCOMMENTSAREPROVIDEDWHENSTUDENTWORKISGRADEDV17THEREISCLOSEAGREEMENTBETWEENSTATEDCOURSEOBJECTIVESANDWHATISTAUGHT322SWANG,HWANG/DATAKNOWLEDGEENGINEERING492004311323ONKNOWLEDGEANDDATAENGINEERING86199692393812TKOHONEN,SELFORGANIZATIONANDASSOCIATIVEMEMORY,THIRDED,SPRINGERVERLAG,BERLIN,198913PLANGLEY,HSIMON,GBRADSHAW,JZYTKOW,SCIENTICDISCOVERYCOMPUTATIONALEXPLORATIONSOFTHECREATIVEPROCESSES,MITPRESS,CAMBRIDGE,MA,1