DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Usage

SAS Enterprise Miner is a full scale commercial product with pricing to match. Version 4 provided many more modelling options that the rewritten Version 5 (which now uses a Java thin client to access the Enterprise Miner server). The missing functionality is working its way back into the product.

Figure: SAS Enterprise Miner interface (Version 4).

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Enterprise Miner works with the idea of projects. The first task is to make a New Project. Supply a Name (this will be the name of a directory) and a Path (which is where the directory will be created). If you have SAS datasets somewhere, you might want to click across to the Startup Code tab and add something like:

  libname dmsource "/home/share/data/sas";

Then select OK to initialise the project.

Now select the new project, and right click the Data Source node. Create a New Data Source, and Browse to the libname you supplied, and choose a SAS dataset from those available. Click on through to either perform a Basic load (automatic processing of variables) or an Advanced load (to allow tuning of how the variables are treated).

Once the Data Source has been defined, create a New Drawing, and then drag the Data Source on to the new drawing. From the Data tab drag the Partition icon onto the diagram and connect the Data Source to the Parition node. Then from the Classify tab drag a decision tree node onto the diagram and connect the Partition node to the Decision Tree node. No right click the Decision Tree node to select Run, to build the decision tree. Right click again, once it has completed, to view the Results.

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