The Apriori algorithm was proposed by Agrawal and Srikant in 1994. Apriori is designed to operate on databases containing transactions, it is often used by grocery stores, retailers, and anyone with a large transactional database. It’s the same way that Target knows your pregnant or when you’re buying an item on Amazon.com they know what else you want to buy. The same idea extends to Pandora.com knowing what song you want to listen to next. All of these incorporate, at some level, data mining concepts and association rule algorithm. Apriori can be split in two steps:

  1. Step 1, find the frequent itemsets: the sets of items that have minimum support – A subset of a frequent itemset must also be a frequent itemset i.e., if {AB} is a frequent itemset, both {A} and {B} should be a frequent itemset – Iteratively find frequent itemsets with cardinality from 1 to k (k-itemset)
  2. Step 2, use the frequent itemsets to generate association rules.