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

Summary

Usage:	Classification tasks, regression and other modelling.
Input:	Training data consisting of entities expressed as attribute-value pairs, with a class associated with each entity.
Output:	An ensemble of models which are to be deployed together with their decisions being combined to give a joint decision.
Complexity:	Depends on complexity of the weak learner employed, but generally the weak learner is quite simple (e.g., OneR or Decision Stumps) hence scalability is generally good.
Availability:	Freely available in Weka (See Chapter ) and in R (See Chapter ). Commercial data mining toolkits implementing AdaBoost include TreeNet (See Chapter ), Statistica (See Chapter ), and Virtual Predict (See Chapter ).

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