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

R

Reflection: The ability of a program to manipulate as data something representing the state of the program during its own execution. Reflection can be introspective or intercessory, or both. Introspection is the ability to observe and analyse one's own state; intercession is the ability to modify one's own execution state or to alter one's own meaning.

Reinforcement Learning: An approach to learning what to do so as to maximise a numerical reward by discovering the most rewarding actions by trying them out (trial-and-error), which itself may affect the immediate reward and subsequent rewards (delayed reward).

Residual: The residual is essentially what is left after you have accounted for in your modelling. It is the difference between the modelled and the actual.

$r^2$: A measure of the closeness of fit of a regression line.

RuleQuest: Ross Quinlan's commercial venture selling the decision tree and rule induction systems C5.0, See5, and Cubist. Refer to the http://www.rulequest.com/Rule Quest Home Page.

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