Affinity analysis is used to determine the likelihood that a set of items will be bought together. In retail, there are natural product affinities; for example, it is very typical for people who buy hamburger patties to buy hamburger rolls, along with ketchup, mustard, tomatoes, and other items that make up the burger experience.

While there are some product affinities that might seem trivial, there are some affinities that are not very obvious. A classic example is toothpaste and tuna. It seems that people who eat tuna are more prone to brushing their teeth right after finishing their meal. So, why it is important for retailers to get a good grasp of product affinities? This information is critical to appropriately plan promotions, as reducing the price for some items may cause a spike on related high-affinity items without the need to further promote these related items.

In the following section, we'll look into the algorithms for association rule learning: Apriori and FP-Growth.