This class of unsupervised ML algorithms helps us understand and extract patterns from transactional datasets. Also termed as Market Basket Analysis (MBA), these algorithms help us identify interesting relationships and associations between items across transactions.

Using association rule mining, we can answer questions like what items are bought together by people at a given store?, or do people who buy wine also tend to buy cheese?, and many more. FP-growth, ECLAT, and Apriori are some of the most widely used algorithms for association rule mining tasks.