DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface List of Figures List of Tables Enterprise Data Mining Process Rattle Data Explore Building Models Evaluation and Deployment Moving into R Troubleshooting R 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

Resources and Further Reading

The original apriori algorithm is due to (). Borgelt (See Chapter 36) provides a freely available, and very efficient, implementation of association analysis, with an extensive collection of measures of interestingness. This same library is directly accessible in R (See Chapter 37) through the arules package described in this chapter.

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