《人工智能与数据挖掘教学课件》l课件

2020/5/5,AI&DM,1,Chapter2DataMining:ACloserLook,2.1DataMiningStrategies,2020/5/5,AI&DM,2,2020/5/5,AI&DM,3,DataMiningStrategies:Classification,Learningissupervised.Thedependentvariableiscategorical(discrete).Well-definedclasses.Outputdependentvariable(因变量)Inputindependentvariable(自变量)Conversionbetweencategorical&numericaldata,2020/5/5,AI&DM,4,DataMiningStrategies:Estimation,Learningissupervised.Thedependentvariableisnumeric.Well-definedclasses.Conversionbetweencategorical&numericaldata,2020/5/5,AI&DM,5,DataMiningStrategies:Prediction,Theemphasisisonpredictingfutureratherthancurrentoutcomes.Theoutputattributemaybecategoricalornumeric.,2020/5/5,AI&DM,6,Classification,EstimationorPrediction?(I),Thenatureofthedatadetermineswhetheramodelissuitableforclassification,estimation,orprediction.Example:Cardiology(心脏病)PatientDatasetThisdatasetcontains303instances.Eachinstanceholdsinformationaboutapatientwhoeitherhasordoesnothaveaheartcondition.138instancesrepresentpatientswithheartdisease.165instancescontaininformationaboutpatientsfreeofheartdisease.,2020/5/5,AI&DM,7,2020/5/5,AI&DM,8,2020/5/5,AI&DM,9,Classification,EstimationorPrediction?(II),Areeitheroftheserulespredictive?HealthyClassRule:IF169=MaximumHeartRate=202THENConceptClass=HealthyRuleaccuracy:85.07%Rulecoverage:34.55%SickClassRule:IFThal=Rev&ChestPainType=AsymptomaticTHENConceptClass=SickRuleaccuracy:91.14%Rulecoverage:52.17%SeeWarningonpage40.,2020/5/5,AI&DM,10,Classification,EstimationorPrediction?（III）,尽管逻辑上，predictingfuture与predictingcurrentoutcomes不同，但解决问题的方法只与数值型还是分类型有关。数据挖掘结果不能告诉我们某种关系是current的还是future的。所以以后的讨论会侧重于是解决categorical的问题还是numerical的问题，忽略是现行关系还是预测未来关系。这也是业界普遍采用的做法，即SupervisedlearningClassificationPrediction,2020/5/5,AI&DM,11,UnsupervisedClusteringcanbeused:,whennodependentvariabletodetermineifrelationshipscanbefoundinthedata.tofindabestsetofinputattributesforsupervisedlearning.todetectOutliers(sofurtherresearchcanbecarriedoutonoutliersorremovethembeforefurtherresearch),2020/5/5,AI&DM,12,DataMiningStrategies:MarketB