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

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. http://en.wikipedia.orgWikipedia 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 http://en.wikipedia.org/wiki/BoostingBoosting. Compare with http://en.wikipedia.org/wiki/variancevariance.

Support further development through the purchase of the PDF version of the book.
Brought to you by Togaware.