09-09-2017, 12:48 PM
Apriori is an algorithm for mining frequent sets of elements and learning association rules on transactional databases. It proceeds by identifying the common individual elements in the database and extending them to ever larger sets of articles, as long as those sets of elements appear frequently enough in the database. Frequent item sets determined by Apriori can be used to determine association rules that highlight general trends in the database: this has applications in domains such as market basket analysis.
The Apriori algorithm was proposed by Agrawal and Srikant in 1994. Apriori is designed to operate on databases that contain transactions (for example, collections of items purchased by customers, or details of a website visitation). Other algorithms are designed to find association rules in data that have no transactions (Winepi and Minepi), or that have no time stamps (DNA sequencing). Each transaction is viewed as a set of elements (a set of elements).
The Apriori algorithm was proposed by Agrawal and Srikant in 1994. Apriori is designed to operate on databases that contain transactions (for example, collections of items purchased by customers, or details of a website visitation). Other algorithms are designed to find association rules in data that have no transactions (Winepi and Minepi), or that have no time stamps (DNA sequencing). Each transaction is viewed as a set of elements (a set of elements).