DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining with Rattle 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 R for the Data Miner 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

Binning

A binning function is provided by Rattle, coded by Daniele Medri. The Rattle interface provides an option to choose between Quantile binning, KMeans binning, and Equal Width binning. For each option the default number of bins is 4, and we can change this to suit our needs. The generated variables are prefixed with either BIN_QUn_, BIN_KMn_, and BIN_EWn_ respectively, with n replaced with the number of bins. Thus, we can create multiple binnings for any variable.

Note that quantile binning is the same as equal count binning.

Figure 5.7: Binning Age.

Figure 5.8: Distributions of binned Age.

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