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Abstract: In a recent paper by Toloo et al. [Toloo, M., Sohrabi, B., & Nalchigar, S. (2009). A new method for ranking discovered rules from data mining by DEA. Expert Systems with Applications, 36, 8503–8508], they proposed a new integrated data envelopment analysis model to find most efficient association rule in data mining. Then, utilizing this model, an algorithm is developed for ranking association rules by considering multiple criteria. In this paper, we show that their model only selects one efficient association rule by chance and is totally depended on the solution method or software is used for solving the problem. In addition, it is shown that their proposed algorithm can only rank efficient rules randomly and will fail to rank inefficient DMUs. We also refer to some other drawbacks in that paper and propose another approach to set up a full ranking of the association rules. A numerical example illustrates some contents of the paper. [Copyright &y& Elsevier]

Abstract: Although all university majors are prominent, and the necessity of their presence is of no question, they might not have the same priority basis considering different resources and strategies that could be spotted for a country. Their priorities likely change as the time goes by; that is, different majors are desirable at different time. If the government is informed of which majors could tackle today existing problems of world and its country, it surely more esteems those majors. This paper considers the problem of clustering and ranking university majors in Iran. To do so, a model is presented to clarify the procedure. Eight different criteria are determined, and 177 existing university majors are compared on these criteria. First, by k-means algorithm, university majors are clustered based on similarities and differences. Then, by AHP algorithm, we rank university majors. [Copyright &y& Elsevier]