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

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.
Brought to you by Togaware.