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:	Internal data mining applications with statistical backing.
Techniques:	Associations. Classification using decision trees and logistic regression. Clustering using k-means. Regression using neural networks and generalised linear models. Ensembles and model assessment. Statistical tools.
Platforms:	GNU/Linux, Unix, MSWindows.
Website:	http://www.sas.com.
Pricing:	High end, annual fees, tens or hundreds of thousand dollars.
Vendor:	SAS Institute, Cary, North Carolina.

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