An association rule is a well-known strategy for exploring potential patterns, correlations, and relationships in a variety of datasets and other data sources. Measurement/context and selection are two of the most difficult aspects of developing association rules. How do we determine the degree to which our goal data is related? How do we choose the groups / clusters that an algorithm or experience has identified? We normally employ data mining approaches when dealing with large datasets, and in this situation, the association rules tend to provide us with a large number of rules from which to build a good model.

As a customer, were you happy to be recommended products in e-commerce stores that you didn’t even know you may need or like? If yes, there’s a whole “rule” behind it that we’d be happy to briefly explain. This rule known as “Market Basket Analysis” is a famous example of this type of data mining technology in practice (MBA). When we talk about association rules, we usually refer to MBA. MBA was one of the first areas where association mining was used. We tend to detect correlations between components that occur together more frequently than we would expect if we randomly sampled all the options.