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Apriori (Agrawal 94) employs [[breadth-first search]] and uses a [[hash tree]] structure to count candidate item sets efficiently. The algorithm generates candidate item sets (patterns) of length <math>k</math> from <math>k-1</math> length item sets. Then, the patterns which have an infrequent sub pattern are pruned. According to the [[downward closure lemma]], the generated candidate set contains all frequent <math>k</math> length item sets. Following that, the whole transaction database is scanned to determine frequent item sets among the candidates. For determining frequent items in a fast manner, the algorithm uses a hash tree to store candidate itemsets. Note: A hash tree has item sets at the leaves and [[hash table]]s at internal nodes (Zaki, 99).
Apriori is designed to operate on databases containing transactions (eg: collection of items bought by customers or details of a website frequentation). Other algorithms are designed for finding association rules in data having no transactions (Winepi and Minepi), or having no timestamps (dna sequencing).
== Algorithm ==
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