Association rules are one solution to the Big Data problem. As an unsupervised learning algorithm, they 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 moderately-sized supermarket. Even in this small example, a wealth of associations were identified. Among these, we noted several patterns that may be useful for future marketing campaigns. The same methods applied here are used at much larger retailers on databases many times this size.

In the next chapter, we will examine another unsupervised learning algorithm, which just like association rules, is intended to find patterns within data. But unlike association rules that seek patterns within the features, the methods in the next chapter are concerned with finding connections among the examples.