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

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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)

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.

Suggested Citation

  • Mehdi Toloo & Reza Farzipoor Saen & Majid Azadi, 2015. "Obviating some of the theoretical barriers of data envelopment analysis-discriminant analysis: an application in predicting cluster membership of customers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(4), pages 674-683, April.
    • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:4:p:674-683
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    Citations

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    Cited by:

    1. Ilke Aydogan & Loïc Berger & Vincent Theroude, 2024. "Pay all subjects or pay only some? An experiment on decision-making under risk and ambiguity," Working Papers 2024-iRisk-03, IESEG School of Management.
    2. Ai-bing Ji & Ye Ji & Yanhua Qiao, 2018. "DEA-Based Piecewise Linear Discriminant Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 809-820, April.

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