A note on "A new method for ranking discovered rules from data mining by DEA", and a full ranking approach

Authors:
A. A. Foroughi
Affiliations:
Department of Mathematics, University of Qom, Isfahan old road, Qom, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 3
Cited 1

A data envelopment model for aggregating preference rankings

Management Science
Ranking discovered rules from data mining with multiple criteria by data envelopment analysis

Expert Systems with Applications: An International Journal
A new method for ranking discovered rules from data mining by DEA

Expert Systems with Applications: An International Journal

Alternative mixed integer linear programming models for identifying the most efficient decision making unit in data envelopment analysis

Computers and Industrial Engineering

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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.