Efficient mining of weighted association rules (WAR) 论文

摘要

In this paper, we extend the tradition association rule problem by allowing a weight to be associated with each item in a transaction, to re ect interest intensity of the item within the transaction. This provides us in turn with an opportunity to associate a weight parameter with each item in the resulting association rule. We call it weighted association rule WAR. WAR not only improves the con dence of the rules, but also provides a mechanism to do more effective target marketing by identifying or segmenting customers based on their potential degree of loyalty o r v olume of purchases. Our approach mines WARs by rst ignoring the weight and nding the frequent itemsets via a traditional frequent itemset discovery algorithm, and is followed by i n troducing the weight during the rule generation. It is shown by experimental results that our approach not only results in shorter average execution times, but also produces higher quality results than the generalization of previous known methods on quantitative association rules.