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.
Techniques:	Associations using apriori. Classification using decision trees. Clustering using k-means (See Chapter ) and EM. Regression using regression trees, neural networks, MARS, generalised linear models and CHAID. Miscellaneous techniques including boosting and survival analysis.
Platforms:	MSWindows with potential for clients running in web browsers.
Website:	http://www.statsoft.com
Pricing:	Available from vendor.
Vendor:	StatSoft, Tulsa, Oklahoma.

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