MOOC交通數據挖掘技術（DataMiningforTransportation）-東南大學中國大學慕課答案Test11、問題：WhichoneisnotthedescriptionofDatamining?選項：A、ExtractionofinterestingpatternsorknowledgeB、Explorationsandanalysisbyautomaticorsemi-automaticmeansC、DiscovermeaningfulpatternsfromlargequantitiesofdataD、Appropriatestatisticalanalysismethodstoanalyzethedatacollected正確答案:【Appropriatestatisticalanalysismethodstoanalyzethedatacollected】2、問題：Whichonedescribestherightprocessofknowledgediscovery?選項：A、Selection-Preprocessing-Transformation-Datamining-Interpretation/EvaluationB、Preprocessing-Transformation-Datamining-Selection-Interpretation/EvaluationC、Datamining-Selection-Interpretation/Evaluation-Preprocessing-TransformationD、Transformation-Datamining-election-Preprocessing-Interpretation/Evaluation正確答案:【Selection-Preprocessing-Transformation-Datamining-Interpretation/Evaluation】3、問題：WhichoneisnotbelongtotheprocessofKDD?選項：A、DataminingB、DatadescriptionC、DatacleaningD、Dataselection正確答案:【Datadescription】4、問題：Whichoneisnottherightalternativenameofdatamining?選項：A、KnowledgeextractionB、DataarcheologyC、DatadredgingD、Dataharvesting正確答案:【Dataharvesting】5、問題：Whichoneisnotthenominalvariables?選項：A、OccupationB、EducationC、AgeD、Color正確答案:【Age】6、問題：Whichoneiswrongaboutclassificationandregression?選項：A、Regressionanalysisisastatisticalmethodologythatismostoftenusedfornumericprediction.B、Wecanconstructclassificationmodels(functions)withoutsometrainingexamples.C、Classificationpredictscategorical(discrete,unordered)labels.D、Regressionmodelspredictcontinuous-valuedfunctions.正確答案:【Wecanconstructclassificationmodels(functions)withoutsometrainingexamples.】7、問題：Whichoneiswrongaboutclusteringandoutliers?選項：A、Clusteringbelongstosupervisedlearning.B、Principlesofclusteringincludemaximizingintra-classsimilarityandminimizinginterclasssimilarity.C、Outlieranalysiscanbeusefulinfrauddetectionandrareeventsanalysis.D、Outliermeansadataobjectthatdoesnotcomplywiththegeneralbehaviorofthedata.正確答案:【Clusteringbelongstosupervisedlearning.】8、問題：Aboutdataprocess,whichoneiswrong?選項：A、Whenmakingdatadiscrimination,wecomparethetargetclasswithoneorasetofcomparativeclasses(thecontrastingclasses).B、Whenmakingdataclassification,wepredictcategoricallabelsexcludingunorderedone.C、Whenmakingdatacharacterization,wesummarizethedataoftheclassunderstudy(thetargetclass)ingeneralterms.D、Whenmakingdataclustering,wewouldgroupdatatoformnewcategories.正確答案:【Whenmakingdataclassification,wepredictcategoricallabelsexcludingunorderedone.】9、問題：Outlierminingsuchasdensitybasedmethodbelongstosupervisedlearning.選項：A、正確B、錯誤正確答案:【錯誤】10、問題：Supportvectormachinescanbeusedforclassificationandregression.選項：A、正確B、錯誤正確答案:【正確】Test21、問題：Whichisnotthereasonweneedtopreprocessthedata?選項：A、tosavetimeB、tomakeresultmeetourhypothesisC、toavoidunreliableoutputD、toeliminatenoise正確答案:【tomakeresultmeetourhypothesis】2、問題：Whichisnotthemajortasksindatapreprocessing?選項：A、CleanB、IntegrationC、TransitionD、Reduction正確答案:【Transition】3、問題：HowtoconstructnewfeaturespacebyPCA?選項：A、NewfeaturespacebyPCAisconstructedbychoosingthemostimportantfeaturesyouthink.B、NewfeaturespacebyPCAisconstructedbynormalizinginputdata.C、NewfeaturespacebyPCAisconstructedbyselectingfeaturesrandomly.D、NewfeaturespacebyPCAisconstructedbyeliminatingtheweakcomponentstoreducethesizeofthedata.正確答案:【NewfeaturespacebyPCAisconstructedbyeliminatingtheweakcomponentstoreducethesizeofthedata.】4、問題：Whichoneiswrongaboutmethodsfordiscretization?選項：A、HistogramanalysisandBingingarebothunsupervisedmethods.B、Clusteringanalysisonlybelongstotop-downsplit.C、Intervalmergingbyc2Analysiscanbeappliedrecursively.D、Decision-treeanalysisisEntropy-baseddiscretization.正確答案:【Clusteringanalysisonlybelongstotop-downsplit.】5、問題：WhichoneiswrongaboutEqual-width(distance)partitioningandEqual-depth(frequency)partitioning?選項：A、Equal-widthpartitioningisthemoststraightforward,butoutliersmaydominatepresentation.B、Equal-depthpartitioningdividestherangeintoNintervals,eachcontainingapproximatelysamenumberofsamples.C、Theintervaloftheformeroneisnotequal.D、Thenumberoftuplesisthesamewhenusingthelatterone.正確答案:【Theintervaloftheformeroneisnotequal.】6、問題：Whichoneiswrongwaytonormalizedata?選項：A、Min-maxnormalizationB、SimplescalingC、Z-scorenormalizationD、Normalizationbydecimalscaling正確答案:【Simplescaling】7、問題：Whicharetherightwaytofillinmissingvalues?選項：A、SmartmeanB、ProbablevalueC、IgnoreD、Falsify正確答案:【Smartmean#Probablevalue#Ignore】8、問題：Whicharetherightwaytohandlenoisedata?選項：A、RegressionB、ClusterC、WTD、Manual正確答案:【Regression#Cluster#WT#Manual】9、問題：Whichoneisrightaboutwavelettransforms?選項：A、Wavelettransformsstorelargefractionsofthestrongestofthewaveletcoefficients.B、TheDWTdecomposeseachsegmentoftimeseriesviathesuccessiveuseoflow-passandhigh-passfilteringatappropriatelevels.C、Wavelettransformscanbeusedforreducingdataandsmoothingdata.D、Wavelettransformsmeansapplyingtopairsofdata,resultingintwosetofdataofthesamelength.正確答案:【TheDWTdecomposeseachsegmentoftimeseriesviathesuccessiveuseoflow-passandhigh-passfilteringatappropriatelevels.#Wavelettransformscanbeusedforreducingdataandsmoothingdata.】10、問題：Whicharethecommonusedwaystosampling?選項：A、SimplerandomsamplewithoutreplacementB、SimplerandomsamplewithreplacementC、StratifiedsampleD、Clustersample正確答案:【Simplerandomsamplewithoutreplacement#Simplerandomsamplewithreplacement#Stratifiedsample#Clustersample】11、問題：Discretizationmeansdividingtherangeofacontinuousattributeintointervals.選項：A、正確B、錯誤正確答案:【正確】Test31、問題：What'sthedifferencebetweeneagerlearnerandlazylearner?選項：A、Eagerlearnerswouldgenerateamodelforclassificationwhilelazylearnerwouldnot.B、Eagerlearnersclassifytheturplebasedonitssimilaritytothestoredtrainingturplewhilelazylearnernot.C、Eagerlearnerssimplystoredata(ordoesonlyalittleminorprocessing)whilelazylearnernot.D、Lazylearnerwouldgenerateamodelforclassificationwhileeagerlearnerwouldnot.正確答案:【Eagerlearnerswouldgenerateamodelforclassificationwhilelazylearnerwouldnot.】2、問題：HowtochoosetheoptimalvalueforK?選項：A、Cross-validationcanbeusedtodetermineagoodvaluebyusinganindependentdatasettovalidatetheKvalues.B、LowvaluesforK(likek=1ork=2)canbenoisyandsubjecttotheeffectofoutliers.C、Alargekvaluecanreducetheoverallnoisesothevaluefor'k'canbeasbigaspossible.D、Historically,theoptimalKformostdatasetshasbeenbetween3-10.正確答案:【Cross-validationcanbeusedtodetermineagoodvaluebyusinganindependentdatasettovalidatetheKvalues.#LowvaluesforK(likek=1ork=2)canbenoisyandsubjecttotheeffectofoutliers.#Historically,theoptimalKformostdatasetshasbeenbetween3-10.】3、問題：What’sthemajorcomponentsinKNN?選項：A、Howtomeasuresimilarity?B、Howtochoosek?C、Howareclasslabelsassigned?D、Howtodecidethedistance?正確答案:【Howtomeasuresimilarity?#Howtochoosek?#Howareclasslabelsassigned?】4、問題：WhichoneofthefollowingwayscanbeusedtoobtainattributeweightforAttribute-WeightedKNN?選項：A、Priorknowledge/experience.B、PCA,FA(Factoranalysismethod).C、Informationgain.D、Gradientdescent,simplexmethodsandgeneticalgorithm.正確答案:【Priorknowledge/experience.#PCA,FA(Factoranalysismethod).#Informationgain.#Gradientdescent,simplexmethodsandgeneticalgorithm.】5、問題：AtlearningstageKNNwouldfindtheKclosestneighborsandthendecideclassifyKidentifiednearestlabel.選項：A、正確B、錯誤正確答案:【錯誤】6、問題：AtclassificationstageKNNwouldstoreallinstanceorsometypicalofthem.選項：A、正確B、錯誤正確答案:【錯誤】7、問題：Normalizingthedatacansolvetheproblemthatdifferentattributeshavedifferentvalueranges.選項：A、正確B、錯誤正確答案:【正確】8、問題：ByEuclideandistanceorManhattandistance,wecancalculatethedistancebetweentwoinstances.選項：A、正確B、錯誤正確答案:【正確】9、問題：DatanormalizationbeforeMeasureDistancecanavoiderrorscausedbydifferentdimensions,self-variations,orlargenumericaldifferences.選項：A、正確B、錯誤正確答案:【正確】10、問題：Thewaytoobtaintheregressionforanewinstancefromtheknearestneighborsistocalculatetheaveragevalueofkneighbors.選項：A、正確B、錯誤正確答案:【正確】11、問題：Thewaytoobtaintheclassificationforanewinstancefromtheknearestneighborsistocalculatethemajorityclassofkneighbors.選項：A、正確B、錯誤正確答案:【正確】12、問題：ThewaytoobtaininstanceweightforDistance-WeightedKNNistocalculatethereciprocalofthedistancesquaredbetweenobjectandneighbors.選項：A、正確B、錯誤正確答案:【正確】Test41、問題：Whichdescriptionisrightaboutnodesindecisiontree?選項：A、InternalnodestestthevalueofparticularfeaturesB、LeafnodesspecifytheclassC、BranchnodesdecidetheresultD、Rootnodesdecidethestartpoint正確答案:【Internalnodestestthevalueofparticularfeatures#Leafnodesspecifytheclass】2、問題：ComputinginformationgainforcontinuousvalueattributewhenusingID3consistsofthefollowingprocedure:選項：A、SortthevalueAinincreasingorder.B、Considerthemidpointbetweeneachpairofadjacentvaluesasapossiblesplitpoint.C、Selecttheminimumexpectedinformationrequirementasthesplit-point.D、Split.正確答案:【SortthevalueAinincreasingorder.#Considerthemidpointbetweeneachpairofadjacentvaluesasapossiblesplitpoint.#Selecttheminimumexpectedinformationrequirementasthesplit-point.#Split.】3、問題：Whichisthetypicalalgorithmstogeneratetrees?選項：A、ID3B、C4.5C、CARTD、PCA正確答案:【ID3#C4.5#CART】4、問題：Whichoneisrightaboutunderfittingandoverfitting?選項：A、Underfittingmeanspooraccuracybothfortrainingdataandunseensamples.B、Overfittingmeanshighaccuracyfortrainingdatabutpooraccuracyforunseensamples.C、Underfittingimpliesthemodelistoosimplethatweneedtoincreasethemodelcomplexity.D、Overfittingoccurstoomanybranchesthatweneedtodecreasethemodelcomplexity.正確答案:【Underfittingmeanspooraccuracybothfortrainingdataandunseensamples.#Overfittingmeanshighaccuracyfortrainingdatabutpooraccuracyforunseensamples.#Underfittingimpliesthemodelistoosimplethatweneedtoincreasethemodelcomplexity.#Overfittingoccurstoomanybranchesthatweneedtodecreasethemodelcomplexity.】5、問題：Whichoneisrightaboutpre-pruningandpost-pruning?選項：A、Bothofthemaremethodstodealwithoverfittingproblem.B、Pre-pruningdoesnotsplitanodeifthiswouldresultinthegoodnessmeasurefallingbelowathreshold.C、Post-pruningremovesbranchesfroma“fullygrown”tree.D、Thereisnoneedtochooseanappropriatethresholdwhenmakingpre-pruning.正確答案:【Bothofthemaremethodstodealwithoverfittingproblem.#Pre-pruningdoesnotsplitanodeifthiswouldresultinthegoodnessmeasurefallingbelowathreshold.#Post-pruningremovesbranchesfroma“fullygrown”tree.】6、問題：Post-pruninginCARTconsistsofthefollowingprocedure:選項：A、First,considerthecostcomplexityofatree.B、Then,foreachinternalnode,N,computethecostcomplexityofthesubtreeatN.C、AndalsocomputethecostcomplexityofthesubtreeatNifitweretobepruned.D、Atlast,comparethetwovalues.IfpruningthesubtreeatnodeNwouldresultinasmallercostcomplexity,thesubtreeispruned.Otherwise,thesubtreeiskept.正確答案:【First,considerthecostcomplexityofatree.#Then,foreachinternalnode,N,computethecostcomplexityofthesubtreeatN.#AndalsocomputethecostcomplexityofthesubtreeatNifitweretobepruned.#Atlast,comparethetwovalues.IfpruningthesubtreeatnodeNwouldresultinasmallercostcomplexity,thesubtreeispruned.Otherwise,thesubtreeiskept.】7、問題：ThecostcomplexitypruningalgorithmusedinCARTevaluatecostcomplexitybythenumberofleavesinthetree,andtheerrorrate.選項：A、正確B、錯誤正確答案:【正確】8、問題：GainratioisusedasattributeselectionmeasureinC4.5andtheformulaisGainRatio(A)=Gain(A)/SplitInfo(A).選項：A、正確B、錯誤正確答案:【正確】9、問題：Ruleiscreatedforeachpartfromitsroottoitsleafnotes.選項：A、正確B、錯誤正確答案:【正確】10、問題：ID3useinformationgainasitsattributeselectionmeasure.AndtheattributewiththelowestinformationgainischosenasthesplittingattributefornoteN.選項：A、正確B、錯誤正確答案:【錯誤】Test51、問題：WhatthefeatureofSVM?選項：A、Extremelyslow,butarehighlyaccurate.B、Muchlesspronetooverfittingthanothermethods.C、Blackboxmodel.D、Provideacompactdescriptionofthelearnedmodel.正確答案:【Extremelyslow,butarehighlyaccurate.#Muchlesspronetooverfittingthanothermethods.#Provideacompactdescriptionofthelearnedmodel.】2、問題：Whichisthetypicalcommonkernel?選項：A、LinearB、PolynomialC、Radialbasisfunction(Gaussiankernel)D、Sigmoidkernel正確答案:【Linear#Polynomial#Radialbasisfunction(Gaussiankernel)#Sigmoidkernel】3、問題：WhatadaptationscanbemadetoallowSVMtodealwithMulticlassClassificationproblem?選項：A、Oneversusrest(OVR).B、Oneversusone(OVO).C、Errorcorrectinginputcodes(ECIC).D、Errorcorrectingoutputcodes(ECOC).正確答案:【Oneversusrest(OVR).#Oneversusone(OVO).#Errorcorrectingoutputcodes(ECOC).】4、問題：What'stheproblemofOVR?選項：A、Sensitivetotheaccuracyoftheconfidencefiguresproducedbytheclassifiers.B、Thescaleoftheconfidencevaluesmaydifferbetweenthebinaryclassifiers.C、Thebinaryclassificationlearnersseeunbalanceddistributions.D、Onlywhentheclassdistributionisbalancedcanbalanceddistributionsattain.正確答案:【Sensitivetotheaccuracyoftheconfidencefiguresproducedbytheclassifiers.#Thescaleoftheconfidencevaluesmaydifferbetweenthebinaryclassifiers.#Thebinaryclassificationlearnersseeunbalanceddistributions.】5、問題：WhichoneisrightabouttheadvantagesofSVM?選項：A、Theyareaccurateinhigh-dimensionalspaces.B、Theyarememoryefficient.C、Thealgorithmisnotproneforover-fittingcomparedtootherclassificationmethod.D、Thesupportvectorsaretheessentialorcriticaltrainingtuples.正確答案:【Theyareaccurateinhigh-dimensionalspaces.#Theyarememoryefficient.#Thealgorithmisnotproneforover-fittingcomparedtootherclassificationmethod.#Thesupportvectorsaretheessentialorcriticaltrainingtuples.】6、問題：Kerneltrickwasusedtoavoidcostlycomputationanddealwithmappingproblems.選項：A、正確B、錯誤正確答案:【正確】7、問題：ThereisnostructuredwayandnogoldenrulesforsettingtheparametersinSVM.選項：A、正確B、錯誤正確答案:【正確】8、問題：Errorcorrectingoutputcodes(ECOC)isakindofproblemtransformationtechniques.選項：A、正確B、錯誤正確答案:【錯誤】9、問題：Regressionformulasincludingthreetypes:linear,nonlinearandgeneralform.選項：A、正確B、錯誤正確答案:【正確】10、問題：Ifyouhaveabigdataset,SVMissuitableforefficientcomputation.選項：A、正確B、錯誤正確答案:【錯誤】Test61、問題：Whichdescriptionisrighttodescribeoutliers?選項：A、OutlierscausedbymeasurementerrorB、OutliersreflectinggroundtruthC、OutlierscausedbyequipmentfailureD、Outliersneededtobedroppedoutalways正確答案:【Outlierscausedbymeasurementerror#Outliersreflectinggroundtruth#Outlierscausedbyequipmentfailure】2、問題：Whatisapplicationcaseofoutliermining?選項：A、TrafficincidentdetectionB、CreditcardfrauddetectionC、NetworkintrusiondetectionD、Medicalanalysis正確答案:【Trafficincidentdetection#Creditcardfrauddetection#Networkintrusiondetection#Medicalanalysis】3、問題：Whichoneisthemethodtodetectoutliers?選項：A、Statistics-basedapproachB、Distance-basedapproachC、Bulk-basedapproachD、Density-basedapproach正確答案:【Statistics-basedapproach#Distance-basedapproach#Density-basedapproach】4、問題：Howtopicktherightkbyaheuristicmethodfordensity-basedoutlierminingmethod?選項：A、Kshouldbeatleast10toremoveunwantedstatisticalfluctuations.B、Pick10to20appearstoworkwellingeneral.C、Picktheupperboundvalueforkasthemaximumof“closeby”objectsthatcanpotentiallybeglobaloutliers.D、Picktheupperboundvalueforkasthemaximumof“closeby”objectsthatcanpotentiallybelocaloutliers.正確答案:【Kshouldbeatleast10toremoveunwantedstatisticalfluctuations.#Pick10to20appearstoworkwellingeneral.#Picktheupperboundvalueforkasthemaximumof“closeby”objectsthatcanpotentiallybelocaloutliers.】5、問題：Whichoneisrightaboutthreemethodsofoutliermining?選項：A、Statistics-basedapproachissimpleandfastbutdifficulttodealwithperiodicitydataandcategoricaldata.B、Theefficiencyofdistance-basedapproachislowforthegreatdatasetinhighdimensionalspace.C、Distance-basedapproachcannotbeusedinmultidimensionaldataset.D、Density-basedapproachspendslowcostonsearchingneighborhood.正確答案:【Statistics-basedapproachissimpleandfastbutdifficulttodealwithperiodicitydataandcategoricaldata.#Theefficiencyofdistance-basedapproachislowforthegreatdatasetinhighdimensionalspace.】6、問題：Distance-basedoutlierMiningisnotsuitabletodatasetthatdoesnotfitanystandarddistributionmodel.選項：A、正確B、錯誤正確答案:【錯誤】7、問題：Statistic-basedmethodneedstorequireknowingthedistributionofthedataandthedistributionparametersinadvance.選項：A、正確B、錯誤正確答案:【正確】8、問題：Whenidentifyingoutlierswithadiscordancytest,thedatapointisconsideredasanoutlierifitfallswithintheconfidenceinterval.選項：A、正確B、錯誤正確答案:【錯誤】9、問題：MahalanobisDistanceaccountsfortherelativedispersionsandinherentcorrelationsamongvectorelements,whichisdifferentfromEuclideanDistance.選項：A、正確B、錯誤正確答案:【正確】10、問題：Anoutlierisadataobjectthatdeviatessignificantlyfromtherestoftheobjects,asifitweregeneratedbyadifferentmechanism.選項：A、正確B、錯誤正確答案:【正確】Test71、問題：Howtodealwithimbalanceddatain2-classclassification?選項：A、OversamplingB、UndersamplingC、Threshold-movingD、Ensembletechniques正確答案:【Oversampling#Undersampling#Threshold-moving#Ensembletechniques】2、問題：Whichoneisrightwhendealingwiththeclass-imbalanceproblem？選項：A、Oversamplingworksbydecreasingthenumberofminoritypositivetuples.B、Undersamplingworksbyincreasingthenumberofmajoritynegativetuples.C、Smotealgorithmaddssynthetictuplesthatareclosetotheminoritytuplesintuplespace.D、Threshold-movingandensemblemethodswereempiricallyobservedtooutperformoversamplingandundersampling.正確答案:【Smotealgorithmaddssynthetictuplesthatareclosetotheminoritytuplesintuplespace.#Threshold-movingandensemblemethodswereempiricallyobservedtooutperformoversamplingandundersampling.】3、問題：Whichstepisnecessarywhenconstructinganensemblemodel?選項：A、CreatingmultipledatasetB、ConstructingasetofclassifiersfromthetrainingdataC、CombiningpredictionsmadebymultipleclassifierstoobtainfinalclasslabelD、Findthebestperformingpredictionstoobtainfinalclasslabel正確答案:【Creatingmultipledataset#Constructingasetofclassifiersfromthetrainingdata#Combiningpredictionsmadebymultipleclassifierstoobtainfinalclass