Apriori- A Big Data Analysis – A Review

Abstract

Apriori is designed to operate on databases containing transactions (for example, collections of items bought by customers, or details of a website frequentation). Other algorithms are designed for finding association rules in data having no transactions , or having no timestamps (DNA sequencing). Each transaction is seen as a set of items (an item set). Given a threshold , the Apriori algorithm identifies the item sets which are subsets of at least transactions in the database. Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as candidate generation), and groups of candidates are tested against the data. The algorithm terminates when no further successful extensions are found.