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

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 ) and integrated within R as part of the arules package (See Chapter ). Most commercial data mining toolkits include an implementation of apriori. Examples include SPSS (See Chapter ), SAS Enterprise Miner (See Chapter ), and Statistica (See Chapter ).

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