Generating non-redundant association rules

The traditional association rule mining framework produces many redundant rules. The extent of redundancy is a lot larger than previously suspected. We present a new framework for associations based on the concept of closed frequent itemset-s. The number of non-redundant rules produced by the new approach is exponentially (in the length of the longest fre-quent itemset) smaller than the rule set from the traditional approach. Experiments using several “hard ” as well as “easy” real and synthetic databases confirm the utility of our frame-work in terms of reduction in the number of rules presented to the user, and in terms of time