Obviating some of the theoretical barriers of data envelopment analysis-discriminant analysis: an application in predicting cluster membership of customers

Mehdi Toloo, Reza Farzipoor Saen and Majid Azadi
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Mehdi Toloo: Technical University of Ostrava, Ostrava, Czech Republic
Reza Farzipoor Saen: Karaj Branch, Islamic Azad University, Karaj, Iran
Majid Azadi: Karaj Branch, Islamic Azad University, Karaj, Iran

Journal of the Operational Research Society, 2015, vol. 66, issue 4, 674-683

Abstract: Data envelopment analysis-discriminant analysis (DEA-DA) has been used for predicting cluster membership of decision-making units (DMUs). One of the possible applications of DEA-DA is in the marketing research area. This paper uses cluster analysis to cluster customers into two clusters: Gold and Lead. Then, to predict cluster membership of new customers, DEA-DA is applied. In DEA-DA, an arbitrary parameter imposing a small gap between two clusters (η) is incorporated. It is shown that different η leads to different prediction accuracy levels since an unsuitable value for η leads to an incorrect classification of DMUs. We show that even the data set with no overlap between two clusters can be misclassified. This paper proposes a new DEA-DA model to tackle this issue. The aim of this paper is to illustrate some computational difficulties in previous DEA-DA approaches and then to propose a new DEA-DA model to overcome the difficulties. A case study demonstrates the efficacy of the proposed model.

Date: 2015
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