DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface List of Figures List of Tables Process . Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Conditional Trees Decision Trees Hierarchical Clustering Kernel Methods K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products Borgelt Data Mining Suite R Rattle Weka Closed Products C4.5 Clementine Enterprise Miner Equbits Foresight GhostMiner InductionEngine ODM Statistica Data Miner TreeNet Virtual Predict Appendicies 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, and See Chapter .

Invariant Clustering:

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