TY - JOUR
T1 - Obviating some of the theoretical barriers of data envelopment analysis-discriminant analysis
T2 - An application in predicting cluster membership of customers
AU - Toloo, Mehdi
AU - Farzipoor Saen, Reza
AU - Azadi, Majid
N1 - Publisher Copyright:
© 2015 Operational Research Society Ltd.
PY - 2015/4/12
Y1 - 2015/4/12
N2 - 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.
AB - 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.
KW - cluster analysis
KW - data envelopment analysis
KW - data envelopment analysis-discriminant analysis (DEA-DA)
KW - discriminant analysis
UR - http://www.scopus.com/inward/record.url?scp=84924561400&partnerID=8YFLogxK
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U2 - 10.1057/jors.2014.43
DO - 10.1057/jors.2014.43
M3 - Article
AN - SCOPUS:84924561400
SN - 0160-5682
VL - 66
SP - 674
EP - 683
JO - Journal of the Operational Research Society
JF - Journal of the Operational Research Society
IS - 4
ER -