數(shù)據(jù)挖掘a英文考試題目及答案

一、單項選擇題（每題2分，共10題）1.Whichofthefollowingisacommondatapreprocessingstepindatamining?A.DataencryptionB.DatanormalizationC.DatacompressionD.DatahidingAnswer:B2.Indatamining,whatisthepurposeofclustering?A.TopredictfuturevaluesB.TogroupsimilardataobjectsC.Tofindthebest-fitlineD.ToclassifydataintopredefinedclassesAnswer:B3.Whichalgorithmisoftenusedforclassificationtasksindatamining?A.K-meansB.PCA(PrincipalComponentAnalysis)C.DecisionTreeD.AprioriAnswer:C4.Whatdoestheterm'featureselection'refertoindatamining?A.SelectingthemostrelevantfeaturesforanalysisB.SelectingallfeaturesforanalysisC.SelectingrandomfeaturesforanalysisD.SelectingfeaturesbasedontheirsizeAnswer:A5.Theaccuracyofadataminingmodelistypicallymeasuredby?A.ThenumberoffeaturesusedB.ThecomplexityofthealgorithmC.ComparingthepredictedvalueswiththeactualvaluesD.ThetimetakentobuildthemodelAnswer:C6.Whichofthefollowingisasupervisedlearningmethodindatamining?A.K-meansclusteringB.AssociationruleminingC.LinearregressionD.PCAAnswer:C7.Indatamining,whatisoverfitting?A.WhenamodelfitsthetrainingdatatoowellandperformspoorlyonnewdataB.WhenamodeldoesnotfitthetrainingdataatallC.WhenamodelistoosimpleD.WhenamodelhastoofewfeaturesAnswer:A8.Whatisthemaingoalofassociationrulemining?A.TofindrelationshipsbetweenvariablesB.TopredictasinglevariableC.ToclusterdataD.ToreducedatadimensionalityAnswer:A9.WhichdatastructureisoftenusedintheApriorialgorithm?A.TreeB.GraphC.HashtableD.MatrixAnswer:C10.Whichofthefollowingisanexampleofanoutlierindata?A.AvaluethatisveryclosetothemeanB.AvaluethatisexactlythemedianC.AvaluethatissignificantlydifferentfromothervaluesD.AvaluethatisequaltozeroAnswer:C二、多項選擇題（每題2分，共10題）1.Whichofthefollowingaredataminingtasks?A.ClassificationB.RegressionC.ClusteringD.AssociationruleminingAnswer:ABCD2.Whatarethecharacteristicsofagooddataminingalgorithm?A.HighaccuracyB.LowcomplexityC.ScalabilityD.InterpretabilityAnswer:ABCD3.Whichtechniquescanbeusedfordatacleaning?A.RemovingduplicatesB.HandlingmissingvaluesC.CorrectinginconsistentdataD.EncryptingdataAnswer:ABC4.Indatamining,thefollowingareimportantforevaluatingamodel?A.PrecisionB.RecallC.F-measureD.AUC(AreaUndertheCurve)Answer:ABCD5.Whichofthefollowingcanbeusedasinputdatafordatamining?A.StructureddataB.UnstructureddataC.Semi-structureddataD.BinarydataAnswer:ABCD6.Whatarethecommonchallengesindatamining?A.High-dimensionaldataB.NoisydataC.ImbalanceddataD.BigdataAnswer:ABCD7.Whichalgorithmsareusedfordimensionalityreductionindatamining?A.PCAB.LDA(LinearDiscriminantAnalysis)C.SVD(SingularValueDecomposition)D.ICA(IndependentComponentAnalysis)Answer:ABCD8.Whataretheapplicationsofdatamining?A.MarketingB.HealthcareC.FinanceD.ManufacturingAnswer:ABCD9.Whichofthefollowingarepartofthedataminingprocess?A.DatacollectionB.DatapreprocessingC.ModelbuildingD.ModelevaluationAnswer:ABCD10.Whatfactorscanaffecttheperformanceofadataminingmodel?A.ThequalityofthedataB.ThechoiceofalgorithmC.ThesizeofthedatasetD.ThecomputingresourcesavailableAnswer:ABCD三、判斷題（每題2分，共10題）1.Dataminingcanonlybeappliedtonumericaldata.(False)2.Unsupervisedlearningdoesnotrequirelabeleddata.(True)3.Adecisiontreeisalwaysthebestalgorithmforclassification.(False)4.Datanormalizationcanimprovetheperformanceofsomedataminingalgorithms.(True)5.Associationruleminingisasupervisedlearningmethod.(False)6.Themorefeaturesamodelhas,thebetteritwillperform.(False)7.Overfittingcanbecompletelyavoidedindatamining.(False)8.Clusteringresultsarealwaysunique.(False)9.Alldataminingalgorithmsarecomputationallyexpensive.(False)10.Dataminingcanhelpinfrauddetection.(True)四、簡答題（每題5分，共4題）1.Brieflyexplainthedifferencebetweensupervisedandunsupervisedlearningindatamining.Answer:Supervisedlearninguseslabeleddata(inputdatawithknownoutput),andthegoalistopredicttheoutputfornewinput.Unsupervisedlearningusesunlabeleddata,aimingtofindpatterns,groupingsorrelationshipswithinthedata.2.Whatistheimportanceofdatapreprocessingindatamining?Answer:Datapreprocessingisimportantasithelpstocleanthedata(handlemissingvalues,removeduplicatesetc.),transformdata(e.g.normalization),andselectrelevantfeatures.Thisimprovestheperformanceandaccuracyofdataminingmodels.3.Explaintheconceptof'outlier'indatamining.Answer:Anoutlierindataminingisadatapointthatissignificantlydifferentfromotherdatapointsinadataset.Itmaybeduetomeasurementerrororrepresentatrulyrareevent.Outlierscanaffecttheperformanceofdataminingmodels.4.Whatarethestepsinvolvedinbuildingadataminingmodel?Answer:Thestepsincludedatacollection,datapreprocessing,selectinganappropriatealgorithm,buildingthemodel,evaluatingthemodelanddeployingthemodelifsatisfactory.五、討論題（每題5分，共4題）1.Discusshowdataminingcanbeappliedine-commerce.Answer:Ine-commerce,dataminingcanbeusedforcustomersegmentation(clusteringcustomersbasedonbehavior),recommendationsystems(usingassociationruleminingtosuggestproducts),frauddetection(identifyingabnormaltransactions),andpredictingcustomerchurn.2.Explaintheroleoffeatureselectioninimprovingtheperformanceofadataminingmodel.Answer:Featureselectionreducesthenumberofinputfeatures.Thishelpstoavoidoverfitting,reducescomputationalcomplexity,andcanimprovethein