The Apriori algorithm is a popular algorithm developed by Agrawal, Rakesh and Ramakrishnan that for extracting frequent item sets to be used in [[Association Rules|association rule learning]]. It is a sensible default, although its runtime can be quite large (especially when there are a high number of unique items). In such case, it is possible to use some alternative like [[FP-Growth]] or [[FP-Max]]. The algorithm provides a [[Support of an Item Set|support]] value, which represents the fraction of transactions in the database that include a given item set. For example, a support of 0.5 represents that 50% of the transactions include the given item set.