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

Bibliographic Notes

Caruana & Niculescu-Mizil (2006) present a comprehensive empirical comparison of many of the modern model builders. An older comparison is known as the Statlog comparison (King et al., 1995).

The ada package for boosting was implemented by Mark Culp, Kjell Johnson, and George Michailidis, and is described in Culp et al. (2006).

Random forests were introduced by Breiman (2001), building on the concept of bagging (Breiman, 1996) and the random subspace method for decision forests (Ho, 1998). Breiman observed that ``random forests do not overfit.''

Support further development through the purchase of the PDF version of the book.
PDF version is properly formatted and forms a comprehensive book (draft with over 600 pages).
Brought to you by Togaware.