We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an e cient algorithm that generates all signi cant association rules between items in the database. The algorithm incorporates bu er management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the e ectiveness of the algorithm.
Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami