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

E

E-Commerce: Conduct of financial transactions by electronic means, usually in the form of on-line business and trade, and often involving the buying and selling of goods over the Internet.

Ensemble Learning: A generic description for a learning system that builds multiple knowledge structures and combines the outcomes in some way. For example, a voting stratgey might be used where we have generated multiple Decision Trees for the same task. Each decision tree will be applied to an unseen record and the decisions from each tree will be combined in some way. One of the earliest examples was developed by [#!williams:acj.19.2.1987!#] (also see [#!williams:aida.1988!#] and [#!williams:phd.anu.1991!#]) where multiple decision trees were combined using a majority rules voting scheme. The idea of a committee of models arose from earlier work in expert systems and then in machine learning with MIL (Multiple Inductive Learning).

Evolutionary Computation: Motivated by Darwinian Evolution heuristic rules are employed to modify a population of solutions in such a way that each generation of the population tends to be on average better than its predecessor. These algorithms generally have three components (analogous to the theory of evolution): A fitness function which is used to guide in the selection of the fitest individual; Reproduction operators to generate new structures from other structures; Genetic operators to guide the combination of the structures in reproduction.

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