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

Apriori:
Association Analysis

Association analysis identifies relationships or affinities between entities and/or between variables. These relationships are then expressed as a collection of association rules. The approach has been particularly successful in mining very large transaction databases and is one of the core classes of techniques in data mining. A typical example is in the retail business where historic data might identify that customers who purchase the Gladiator DVD and the Patriot DVD also purchase the Braveheart DVD. The historic data might indicate that the first two DVDs are purchased by only 5% of all customers. But 70% of these then also purchase Braveheart. This is an interesting group of customers. As a business we may be able to take advantage of this observation by targetting advertising of the Braveheart DVD to those customers who have purchased both Gladiator and Patriot.

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