Data Mining Using SAS Enterprise Miner: A Case Study

Enterprise MinerSetting Up the Initial Project and DiagramIdentifying the Interface ComponentsDefinition of Data MiningOverview of the DataPredictive and Descriptive TechniquesOverview of SEMMAExplore NodesModify NodesModel NodesAssess NodesScoring NodesUtility NodesSome General Usage Rules for NodesExplorerAdding the NodesUsing the CursorIdentifying Target VariablesInspecting DistributionInspecting Default Settings in the Data Partition NodeFitting and Evaluating a Regression ModelLift ChartUnderstanding Data ReplacementFitting and Evaluating a Regression Model with Data ReplacementModel ResultsRescale AxesEMPROJ.SMP_TRN6 (in this example)Performing Variable TransformationUsing the Replacement NodeMeanSet ValueUnderstanding Interactive GroupingPerforming Interactive GroupingSettingsDataset box.Fitting a Regression ModelInteraction BuilderEvaluating the ModelLift ChartModel NameFitting a Default Decision TreeLift ChartModel NameFitting a Default Neural NetworkInvestigating the Regression and Neural Network ModelsFitting a Regression ModelGenerating and Using Scoring CodeScoring Using Base SASUsing the Variable Selection NodeLift Chart.Overview of Clustering MethodsBuilding the Initial FlowSetting Up the Input Data Source NodeWhen to Use the Replacement NodeInterpreting the ResultsUsing the Insight NodeSetting Up the Input Data Source NodeSetting Up the Association NodeUnderstanding and Choosing Analysis ModesProblem FormulationExamining Web Log DataSetting Up the DiagramShowing PathsNoneEnterprise Miner nodes are arranged into the following categories according the SAS process for data mining: SEMMA. Sample — identify input data sets (identify input data; sample from a larger data set; partition data set into training, validation, and test data sets). Explore — explore data sets statistically and graphically (plot the data, obtain...