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

Decision Tree Induction

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One of the classic machine learning techniques, widely deployed in data mining, is decision tree induction. Using a simple algorithm and a simple knowledge structure, the approach has proven to be very effective. These simple tree structures represent a classification (and regression) model. Starting at the root node, a simple question is asked (usually a test on a variable value, like Age $<$ 35). The branches emanating from the node correspond to alternative answers. For example, with a test of Age $<$ 35 the alternatives would be Yes and No. Once a leaf node is reached (one from which no branches emanate) we take the decision or classification associated with that node. Some form of probability may also be associated with the nodes, indicating a degree of certainty for the decision. Decision tree algorithms handle mixed types of variables, handle missing values, are robust to outliers and monotonic transformations of the input, and robust to irrelevant inputs. Predictive power tends to be poorer than other techniques.

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