Online Generation of Association Rules

We have a large database consisting of sales transactions. We investigate the problem of online mining of association rules in this large database. We show how to preprocess the data e ectively in order to make it suitable for repeated online queries. The preprocessing algorithm takes into account the storage space available. We store the preprocessed data in such a way that online processing may be done by applying a graph theoretic search algorithm whose complexity is proportional to the size of the output. This results in an online algorithm which is practically instantaneous in terms of response time. The algorithm also supports techniques for quickly discovering association rules from large itemsets. The algorithm is capable of nding rules with speci c items in the antecedent or consequent. These association rules are presented in a compact form, eliminating redundancy. We believe that the elimination of redundancy in online generation of association rules from large itemsets is interesting in its own right.