The Apriori algorithm is a classic algorithm used for frequent pattern mining and association rule learning over transactional. By identifying the frequent individual items in a database and extending them to larger itemsets, Apriori can determine the association rules, which highlight general trends about a database.

The Apriori algorithm constructs a set of itemsets, for example, itemset1= {Item A, Item B}, and calculates support, which counts the number of occurrences in the database. Apriori then uses a bottom up approach, where frequent itemsets are extended, one item at a time, and it works by eliminating the largest sets as candidates by first looking at the smaller sets and recognizing that a large set cannot be frequent unless all of its subsets are. The algorithm terminates when no further successful extensions are found.

Although the Apriori algorithm is an important milestone in machine learning, it suffers from a number of inefficiencies and tradeoffs. In the following section, we'll look into a more recent FP-Growth technique.