DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Rattle Introduction Getting Started Interacting with Rattle Data Exploring Data Test Transforming Data Cluster Analysis Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines 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 Installing Rattle 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 Refer[rulequest]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 Referinterestingness incorporating the user's point of view.

Supervised Learning: Training data is labelled by class membership.

Support:

Support further development through the purchase of the PDF version of the book.
PDF version is properly formatted and forms a comprehensive book (draft with over 700 pages).
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