This chapter discusses an unsupervised learning method called association rules. This is a descriptive, not predictive, method often used to discover interesting relationships hidden in a large dataset. The disclosed relationships can be represented as rules or frequent itemsets. Association rules are commonly used for mining transactions in databases.
Here are some possible questions that association rules can answer:
Figure 5-1 shows the general logic behind association rules. Given a large collection of transactions (depicted as three stacks of receipts in the figure), in which each transaction consists of one or more items, association rules go through the items being purchased to see what items are frequently bought together and to discover a list of rules that describe the purchasing behavior. The goal with association rules is to discover interesting relationships among the items. (The relationship occurs too frequently to be random and is meaningful from a business perspective, which may or may not be obvious.) The relationships that are interesting depend both on the business ...