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

R Dataset Option

Rattle can use a dataset that is already loaded into R (although it will take a copy of it, with memory implications). Only data frames are currently supported, and Rattle will list for you the names of all of the available data frames.

The data frames need to be constructed in the same R session that is running Rattle (i.e., the same R Console in which you lo the Rattle package). This provides much more flexibility in loading data into Rattle, than is provided directly through the actual Rattle interface. For example, you may want to load data from an SQLite database directly, and have this available in Rattle.

Support further development through the purchase of the PDF version of the book.
Brought to you by Togaware.