Association rules are used to find useful insight in the massive transaction databases of large retailers. As an unsupervised learning process, association rule learners are capable of extracting knowledge from large databases without any prior knowledge of what patterns to seek. The catch is that it takes some effort to reduce the wealth of information into a smaller and more manageable set of results. The Apriori algorithm, which we studied in this chapter, does so by setting minimum thresholds of interestingness, and reporting only the associations meeting these criteria.

We put the Apriori algorithm to work while performing a market basket analysis for a month's worth of transactions at a modestly sized supermarket. Even in this small example, a wealth of associations was identified. Among these, we noted several patterns that may be useful for future marketing campaigns. The same methods we applied are used at much larger retailers on databases many times this size, and can also be applied to projects outside of a retail setting.

In the next chapter, we will examine another unsupervised learning algorithm. Just like association rules, it is intended to find patterns within data. But unlike association rules that seek groups of related items or features, the methods in the next chapter are concerned with finding connections and relationships among the examples.