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 ...
