Apriori algorithm: Difference between revisions

Revision as of 23:49, 12 December 2006

Revision as of 23:55, 12 December 2006

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In [[computer science]] and [[data mining]], '''Apriori''' is the name of one of the first publishedan algorithms for [[data mining|mining data]] forlearning [[association rule]]s. It was developed by Rakesh Agrawal, et al. Apriori is designed to operate on [[database]]s 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 (Winepi and Minepi), or having no timestamps (DNA sequencing).

As is common in association rule mining, given a set of ''itemsets'' (for instance, sets of retail transactions each listing individual items purchased), the algorithm attempts to find subsets which are common to at least a minimum number C (the cutoff, or confidence threshold) of the itemsets. 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.