Mining Generalized Association Rules

We introduce the problem of mining generalized association rules. Given a large database of transactions, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items, we find associations between items at any level of the taxonomy. For example, given a taxonomy that says that jackets is-a outerwear is-e clothes, we may infer a rule that “people who buy outerwear tend to buy shoes”. This rule may hold even if rules that “people who buy jackets tend to buy shoes”, and “people who buy clothes tend to buy shoes” do not hold. An obvious solution to the problem is to add all ancestors of each item in a transaction to the transaction, and then run any of the algorithms for mining association rules on these “extended transactions” . However, this “Basic” algorithm is not very fast; we present two algorithms, Cumulate and EstMerge, which run 2 to 5 times faster than Basic (and more than 100 times faster on one real-life dataset). We also present a new interest-measure for rules which uses the information in the taxonomy. Given a user-specified “minimum-interest-level”, this measure prunes a large number of redundant rules; 40% to 60% of all the rules were pruned on two real-life datasets. *Also, Department of Computer Science, University of Wisconsin, Madison. Permission to copy without fee all OT part of this material is granted provided that the copies are not made OT distrib&ed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying ia by permission of the Very Large Data Base Endowment. To copy otherwise, OT to republish, requires a fee and/or special pcTmiasion from the Endowment. Proceedings of the 21st VLDB Conference Zurich, Swizerland, 1995