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 language | English |
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Pages (from-to) | 674-683 |
Number of pages | 10 |
Journal | Journal of the Operational Research Society |
Volume | 66 |
Issue number | 4 |
DOIs | |
Publication status | Published - Apr 12 2015 |
Externally published | Yes |
Keywords
- cluster analysis
- data envelopment analysis
- data envelopment analysis-discriminant analysis (DEA-DA)
- discriminant analysis
ASJC Scopus subject areas
- Management Information Systems
- Strategy and Management
- Management Science and Operations Research
- Marketing