DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Introduction Data Mining with Rattle Data Exploring Data Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting Beyond Rattle R 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 GTK, R, and Rattle Glossary Bibliography Index

I

Independent Variable: The classical term for an input variable.

Input Variable: The variables of a dataset that are generally measurable or preset, and used as the input to the modelling and mining. It is synonymous with the statistical term predictor and the classical term independent variable. See also Output Variable.

Interestingness: A measure used to rank or filter discoveries, particularly in the context of the often overwhelming number of discoveries that typical data mining tools present the user, with example measures including support, confidence, lift, and leverage. See also subjective interestingness, objective interestingness.

Invariant Clustering:

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 600 pages).
Brought to you by Togaware.