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

Further Information

A reference card is available from http://www.rpad.org/Rpad/Rpad-refcard.png

Useful books include:

bootstrapBootstrap Methods and Their ApplicationsAnthony C. Davison and David V. Hinkley256 pages published by Cambridge University Press, 1994, ISBN 0-521-57391-2. The R package boot is based on the methods discussed in this book.

ToDo: Add Hastie, Tinshirani, Friedman. Add Venables, Ripley. Add Daalgaard.

Support further development through the purchase of the PDF version of the book.
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