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

The Initial Interface

The user interface for Rattle is designed to flow through the data mining process, progressing through the Tabs from left to right. Here we illustrate the interface for the Two Class paradigm.

The process in Rattle is:

1. Load a Dataset;
2. Select Variables for exploring and mining;
3. Transform the data into training and test datasets;
4. Explore the data;
5. Build your Models;
6. Evaluate the models;
7. Review the Log of the data mining process.

The collection of Paradigms, displayed as radio buttons to the right of the buttons on the toolbar, allow a multitude of Rattle functionality to be shared while supporting a variety of different types of tasks.

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