Chapter 16 ASSOCIATION RULES

Keywords: Association rules are rules of the kind "70% of the customers who buy vine and cheese also buy grapes". While the traditional field of application is market basket analysis, association rule mining has been applied to various fields since then, which has led to a number of important modifications and extensions. We discuss the most frequently applied approach that is central to many extensions, the Apriori algorithm, and briefly review some applications to other data types, well-known problems of rule evaluation via support and confidence, and extensions of or alternatives to the standard framework. 1 Introduction To increase sales rates at retail a manager may want to offer some discount on certain products when bought in combination. Given the thousands of products in the store, how should they be selected (in order to maximize the profit)? Another possibility is to simply locate products which are often purchased in combination close to each other, to remind a customer, who just rushed into the store to buy product A, that she or he may also need product B. This may prevent the customer from visiting a-possibly different-store to buy B a short time after. The idea of "market basket analysis", the prototypical application of association rule mining, is to find such related products by analysing the content of the customer's market basket to find product associations like "70% of the customers who buy vine and cheese also buy grapes." The task is to find associated products within the set of offered products, as a support for marketing decisions in this case. Thus, for the traditional form of association rule mining the database schema S = {AI, ..., A,) consists of a large number of attributes (n is in the range of several hundred) and the attribute domains are binary, that is, dom(Ai) =