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

Algorithm

Decision tree induction algorithms are generally what are called greedy algorithms. They are greedy in that they decide on a question to ask then don't consider any more alternatives later on. The algorithms are also dived and conquer because they partition the database into smaller sets. In fact, the general algorithm continually partitions the database into smaller sets until the sets all have the same value for the output variable.

Note that pruning is a mechanism for reducing the variance of the resulting models. However, for large datasets the reduction of variance is not usually useful thus unpruned trees may actually be better.

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