DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface List of Figures List of Tables Process . Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Conditional Trees Decision Trees Hierarchical Clustering Kernel Methods K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products Borgelt Data Mining Suite R Rattle Weka Closed Products C4.5 Clementine Enterprise Miner Equbits Foresight GhostMiner InductionEngine ODM Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Summary

Complexity:

Generally C4.5 is quite efficient as the number of training instances increases, and for specific datasets has been found empirically to be between and . With rule generation the algorithm is somewhat more expensive at .

Availability:

The Borgelt collection (See Chapter 25) contains dtree, a generic implementation of the decision tree divide and conqueror algorithm. A Gnome-based graphical front-end is available as part of the Gnome Data Mining Toolkit (See Chapter ). Weka (See Chapter 28) also provides a freely available implementation of a decision tree induction algorithm (J48) within its Java-based framework. Decision tree induction is a fundamental data mining tool and implementations of C4.5 or its variations are available in most commercial data mining toolkits, including Clementine (See Chapter 30) and STATISTICA (See Chapter 36).

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