Association rules are rule-based machine learning methods for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.
These rules are often if/then statements that help uncover relationships between seemingly unrelated data in a relational database or other information repository. An example of an association rule would be "If a customer buys a dozen eggs, he is 80% likely to also purchase milk."
Piatetsky-Shapiro, Gregory (1991), Discovery, analysis, and presentation of strong rules, in Piatetsky-Shapiro.
Hahsler, Michael (2005). Introduction to arules – A computational environment for mining association rules and frequent item sets. Journal of Statistical Software.