DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Introduction Data Mining with Rattle Data Exploring Data Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting Beyond Rattle R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines 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 Installing GTK, R, and Rattle Glossary Bibliography Index

Delete Ignored

As an example, suppose we have loaded our familiar audit dataset. In the Select tab choose to set the role of Age, Employment, Education, Marital, and Occupation to be Ignore, and then click the Execute button for these new roles to take effect. In the R console window use the XnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)R functionsR functionsobject.size to determine the current amount of memory the dataset is taking up:

>  object.size(crs$dataset)
[1] 128904

Now, back in the Rattle window, navigate to the Transform tab and choose the Cleanup option. The Delete Ignored sub-option is the default. Click the Execute button to remove the columns that we marked as Ignored. Now in the R console, check how much space the dataset is taking up:

> object.size(crs$dataset)
[1] 84216

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