Affinity analysis is the heart of Market Basket Analysis (MBA). It can discover co-occurring relationships among activities performed by specific users or groups. In retail, affinity analysis can help you understand the purchasing behavior of customers. These insights can drive revenue through smart cross-selling and upselling strategies and can assist you in developing loyalty programs, sales promotions, and discount plans.

In this chapter, we will look into the following topics:

• MBA
• Association rule learning
• Other applications in various domains

First, we will revise the core association rule-learning concepts and algorithms, such as support and lift Apriori algorithms and the FP-Growth algorithm. Next, we will use Weka to perform our first affinity analysis on a supermarket dataset and study how to interpret the resulting rules. We will conclude this chapter by analyzing how association rule learning can be applied in other domains, such as IT operations analytics, and medicine.