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Various rules can be generated from databases by using association rule algorithms, but only a small number of these rules may be selected for implementation due to the limitations of resources. Accordingly, evaluating the quality of these rules becomes a hot topic in the data mining field. Based on multiple criteria decision theory, a framework for evaluating the mined association rules using TOPSIS method with combination weights is proposed, which takes into account both objective interestingness measures and the users’ domain information. An example of market basket analysis is applied to illustrate the applicability of this method.