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:	Oracle database users.
Techniques:	Classification: Decision Trees, Support Vector Machines, Naive Bayes, Adaptive Bayes Network. Regression: Support Vector Machines. Clustering: Hierarchical K-Means, Orthogonal partitioned cluster (O-Cluster) Feature Extraction: Non-negative matrix factorization
Platforms:	All Oracle10g platforms, including Linux, Unix, MacOS, MSWindows.
Website:	http://www.oracle.com/technology/products/bi/odm/
Vendor:	http://www.oracle.comOracle, California.

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