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

S

Sample: In statistics a large sample is anything 30 or more!

See5: A commercial Windows95/NT decision tree and rule induction product from RuleQuest developed by Ross Quinlan as the successor to his very successful and widely used ID3 and C4.5 systems.

Segmentation:

Similarity-Based Learning: Involves comparing several examples of a concept, searching for common variables, in order to define the concept being learned.

Statistics:

Subjective Interestingness: A measure of interestingness incorporating the user's point of view.

Supervised Learning: Training data is labelled by class membership.

Support:

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