Mining Association Rules between Sets of Items in Large Databases Export

@proceedings{AgrawalEtAl93, abstract = {We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer 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 effectiveness of the algorithm.}, address = {Washington, D.C.}, author = {Agrawal, Rakesh and Imielinski, Tomasz and Swami, Arun N.}, booktitle = {Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data}, citeulike-article-id = {3564}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.6984}, comment = {still nothing about stat sig, except that they think it is completely covered by support. they mention that baskets can be bucketed arbitrarily over time. one more time: support = \# baskets that have both sides of rule/ total \# of baskets 2^m possible itemsets}, editor = {Buneman, Peter and Jajodia, Sushil}, keywords = {association, datamining, rules}, month = {FebruaryJune--FebruaryAugust\~{}}, pages = {207--216}, posted-at = {2007-09-18 20:38:20}, priority = {0}, title = {Mining Association Rules between Sets of Items in Large Databases}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.6984}, year = {1993} }

TY - CONF ID - AgrawalEtAl93 L3 - citeulike-article-id:3564 N2 - We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer 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 effectiveness of the algorithm. AD - Washington, D.C. EP - 216 TI - Mining Association Rules between Sets of Items in Large Databases T2 - Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data SP - 207 KW - association KW - datamining KW - rules N1 - still nothing about stat sig, except that they think it is completely covered by support. they mention that baskets can be bucketed arbitrarily over time. one more time: support = # baskets that have both sides of rule/ total # of baskets 2^m possible itemsets AU - Agrawal, Rakesh AU - Imielinski, Tomasz AU - Swami, Arun A2 - Buneman, Peter A2 - Jajodia, Sushil PY - 1993/26--28~// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.6984 ER -