In this paper, we extend the tradition association rule problem by allowing a weight to be associated with each item in a transaction, to reflect 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 confidence 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 or volume of purchases. Our approach mines WARs by first ignoring the weight and finding the frequent itemsets (via a traditional frequent itemset discovery algorithm), and is followed by introducing 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.