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

Summary

Usage:	Market basket analysis, customer relationship management.
Input:	Transactional data consisting of items whose co-occurrence within a transaction is of interest.
Output:	Association rules identifying relationships between items within each transaction.
Complexity:	Adversely affected as the support threshold decreases, particularly for large datasets. Exponential.
Availability:	Freely available as part of the Borgelt Suite (See Chapter 36) with a Gnome-based front-end available as part the Gnome Data Mining Toolkit (See Chapter ), and integrated within R as part of the arules package (See Chapter 37). Most commercial data mining toolkits include an implementation of apriori. Examples include SPSS (See Chapter 41), SAS Enterprise Miner (See Chapter 42), and Statistica (See Chapter 47).

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