New Algorithms for Fast Discovery of Association Rules

Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets, and then forming conditional implication rules among them. In this paper we present e cient algorithms for the discovery of frequent itemsets, which forms the compute intensive phase of the task. The algorithms utilize the structural properties of frequent itemsets to facilitate fast discovery. The related database items are grouped together into clusters representing the potential maximal frequent itemsets in the database. Each cluster induces a sub-lattice of the itemset lattice. E cient lattice traversal techniques are presented, which quickly identify all the true maximal frequent itemsets, and all their subsets if desired. We also present the e ect of using di erent database layout schemes combined with the proposed clustering and traversal techniques. The proposed algorithms scan a (pre-processed) database only once, addressing the open question in association mining, whether all the rules can be e ciently extracted in a single database pass. We experimentally compare the new algorithms against the previous approaches, obtaining improvements of more than an order of magnitude for our test databases. The University of Rochester Computer Science Department supported this work. This work was supported in part by an NSF Research Initiation Award (CCR-9409120) and ARPA contract (F19628-94-C-0057).