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, Majid Azadi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)674-683
Number of pages10
JournalJournal of the Operational Research Society
Volume66
Issue number4
DOIs
Publication statusPublished - 21 Dec 2017

Bibliographical note

Copyright © 2015 Operational Research Society Ltd

Funding Information:
The research was supported by the Czech Science Foundation (GACR project 14-31593S) and through the European Social Fund within the project CZ.1.07/2.3.00/ 20.0296.

Keywords

  • cluster analysis
  • data envelopment analysis
  • data envelopment analysis-discriminant analysis (DEA-DA)
  • discriminant analysis

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