Discovery of association rules is a very hot topic in data mining research, which has been found applicable and useful in many areas. In the current researches, all the items in a databases are treated in a uniform way. However, it is not true in the real world databases, in which different items usually have different importances. In order to represent the importance of individual items, the weight value for items is introduced, and a new problem of discovery of weighted association rules is put forward. Due to the introduction of weight for items, it is not sure that any subset of a frequent itemset is also frequent. Thus, a concept of k-support bound of itemsets is set forth, and an algorithm to discover weighted association rules is proposed.