You would have to live on the dark side of the Moon to not see the results of the techniques that we're about to discuss in this chapter every day. If you visit www.amazon.com, watch movies on www.netflix.com, or visit any retail website, you'll be exposed to terms such as related products, because you watched..., customers who bought x also bought y, and recommended for you, at every twist and turn. With large volumes of historical real-time or near real-time information, retailers utilize various algorithms in an attempt to increase both the quantity of buyers' purchases and the value of those purchases. 

The techniques to do this can be broken down into two categories: association rules and recommendation engines. Association rule analysis is commonly referred to as market basket analysis, as it's concerned with understanding what items are purchased together. With recommendation engines, the goal is to provide a customer with other items that they'll enjoy based on how they've rated items they've viewed or purchased previously.

In this chapter, we'll focus on association analysis. It's applicable not only to making recommendations, product placement, and promotional pricing, but can be used in manufacturing, web usage, healthcare, and so on. If you're interested in how items occur together, apply what you're about to learn.