DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining with Rattle 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 R for the Data Miner 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

Glossary

With the rather unruly explosion of interest in data mining and its well documented commercial successes, and the fact that data mining is the fusion of many disciplines, each with their own heritage, the terminology used in the data mining community is at times quite confusing and often redundant. The beginnings of a Glossary began here but has ceased. Wikipedia is now the canonical source.

Bias: The error in a model due to systematic inadequacies in the learning process. That is, those instances consistently incorrectly classified by models built by the learning algorithm. Modelling error due to bias can be reduced using Boosting. Compare with variance.

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