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

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 $O(n^{1.22})$ and $O(n^{1.38})$. With rule generation the algorithm is somewhat more expensive at $O(n^4)$.

Availability:

The Borgelt collection (See Chapter ) contains dtree, a generic implementation of the decision tree divide and conqueror algorithm. Weka (See Chapter ) 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 ) and STATISTICA (See Chapter ).

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