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

Summary

Market:	General data mining applications, ease of use, ideal for new data miners.
Techniques:	Associations using apriori (arules), Classification using decision trees (rpart), generalised linear models (glm), boosting (gbm), and Random Forests (randomForest).
Platforms:	GNU/Linux, Unix, MS/Windows, Macintosh/OSX
Website:	http://www.togaware.com/datamining/
Pricing:	Freely available--GNU General Public License.
Vendor:	Togaware, Canberra, Australia.

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