Association rule learning has been a popular approach to discover interesting relationships among items in large databases. It is most commonly applied in retail to reveal regularities between products.

Association rule learning approaches find patterns as interesting strong rules in the database using different measures of interestingness. For example, the following rule would indicate, that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat: {onions, potatoes} -> {burger}.

Another classic story probably told in every machine-learning class is the beer and diaper story. An analysis of supermarket shoppers' behavior showed that customers, presumably young men, who buy diapers also tend to buy beer. It immediately became a popular example of how an unexpected association rule might be found from everyday data; however, there are varying opinions as to how much of the story is true. In DSS News 2002, Daniel Powers says this:

In addition to the preceding example from MBA, association rules are today employed in many application areas, including web usage mining, intrusion detection, continuous production, and bioinformatics. We'll take a closer look at these areas later in this chapter.