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

Overview

Decision tree analysis is a technique for building classification models for predicting classes for unseen entities.

Decision trees have been around for a long time as a mechanism for structuring a series of questions and choosing the next question to ask on the basis of the answer to the previous question. In data mining we commonly identify decision trees as the knowledge representation scheme targeted by the family of techniques originating from ID3 in 1979.

As a classification technique decision tree analysis generates decision trees for discrete value classification. The variables can be either continuous or categorical.

A decision tree has the traditional computer science structure of a tree, having a single root node and multiple arcs emanating from the node to connect to other nodes.

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