Special Issue Article
The Apriori algorithm: Data Mining Approaches Is To Find Frequent Item Sets From A Transaction Dataset
Aprioriis an algorithm for learning association rules. Apriori is designed to operate on databases containing transactions. As is common in association rule mining, given a set of item sets, the algorithm attempts to find subsets which are common to at least a minimum number candidate C of the item sets. Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time, and groups of candidates are tested against the data. The algorithm terminates when no further successful extensions are found. The purpose of the Apriori Algorithm is to find associations between different sets of data. It is sometimes referred to as "Market Basket Analysis". Each set of data has a number of items and is called a transaction. The output of Apriori is sets of rules that tell us how often items are contained in sets of data.