Association-rule mining, or market-basket analysis, is a very popular data mining technique used in the retail industry to identify the products that need to be kept together so as to encourage cross sales. An interesting aspect behind this algorithm is that historical invoices are mined to identify the products that are bought together.

There are several off-the-shelf algorithms available to perform market-basket analysis. Some of them are Apriori, equivalence class transformation (ECLAT), and frequent pattern growth (FP-growth). We will learn to solve our problem of recommending jokes to users through applying the Apriori algorithm on the Jester jokes dataset. We will now learn the theoretical aspects that underpin the Apriori algorithm.