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Some clarifications on the DEA clustering approach

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  • Amin, Gholam R.
  • Emrouznejad, Ali
  • Rezaei, S.

Abstract

This paper clarifies the role of alternative optimal solutions in the clustering of multidimensional observations using data envelopment analysis (DEA). The paper shows that alternative optimal solutions corresponding to several units produce different groups with different sizes and different decision making units (DMUs) at each class. This implies that a specific DMU may be grouped into different clusters when the corresponding DEA model has multiple optimal solutions.

Suggested Citation

  • Amin, Gholam R. & Emrouznejad, Ali & Rezaei, S., 2011. "Some clarifications on the DEA clustering approach," European Journal of Operational Research, Elsevier, vol. 215(2), pages 498-501, December.
    • Handle: RePEc:eee:ejores:v:215:y:2011:i:2:p:498-501
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    References listed on IDEAS

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    1. Cooper, William W. & Ruiz, Jose L. & Sirvent, Inmaculada, 2007. "Choosing weights from alternative optimal solutions of dual multiplier models in DEA," European Journal of Operational Research, Elsevier, vol. 180(1), pages 443-458, July.
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    5. Rajiv Banker & Hsihui Chang & Ram Natarajan, 2007. "Estimating DEA technical and allocative inefficiency using aggregate cost or revenue data," Journal of Productivity Analysis, Springer, vol. 27(2), pages 115-121, April.
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    7. Po, Rung-Wei & Guh, Yuh-Yuan & Yang, Miin-Shen, 2009. "A new clustering approach using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 199(1), pages 276-284, November.
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    Cited by:

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    2. Li, Yang, 2020. "Analyzing efficiencies of city commercial banks in China: An application of the bootstrapped DEA approach," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    3. Ghasemi, M.-R. & Ignatius, Joshua & Emrouznejad, Ali, 2014. "A bi-objective weighted model for improving the discrimination power in MCDEA," European Journal of Operational Research, Elsevier, vol. 233(3), pages 640-650.
    4. Kim, Nam Hyok & He, Feng & Zhang, Hongjie & Hong, Kwon Ryong & Ri, Kwang-Chol, 2023. "A data envelopment analysis-based clustering approach under dynamic situations," European Journal of Operational Research, Elsevier, vol. 311(1), pages 251-262.
    5. Yang Li & An-Chi Liu & Shu-Mei Wang & Yiting Zhan & Jingran Chen & Hsiao-Fen Hsiao, 2022. "A Study of Total-Factor Energy Efficiency for Regional Sustainable Development in China: An Application of Bootstrapped DEA and Clustering Approach," Energies, MDPI, vol. 15(9), pages 1-13, April.
    6. Mohammed Al-Siyabi & Gholam R. Amin & Shekar Bose & Hussein Al-Masroori, 2019. "Peer-judgment risk minimization using DEA cross-evaluation with an application in fishery," Annals of Operations Research, Springer, vol. 274(1), pages 39-55, March.
    7. Tsionas, Mike G., 2023. "Clustering and meta-envelopment in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 304(2), pages 763-778.
    8. Afsharian, Mohsen & Bogetoft, Peter, 2023. "Limiting flexibility in nonparametric efficiency evaluations: An ex post k-centroid clustering approach," European Journal of Operational Research, Elsevier, vol. 311(2), pages 633-647.
    9. Yang Li & An-Chi Liu & Yi-Ying Yu & Yueru Zhang & Yiting Zhan & Wen-Cheng Lin, 2022. "Bootstrapped DEA and Clustering Analysis of Eco-Efficiency in China’s Hotel Industry," Sustainability, MDPI, vol. 14(5), pages 1-16, March.
    10. Llorca, Manuel & Orea, Luis & Pollitt, Michael G., 2014. "Using the latent class approach to cluster firms in benchmarking: An application to the US electricity transmission industry," Operations Research Perspectives, Elsevier, vol. 1(1), pages 6-17.
    11. Lee, Jiyoung & Kim, Chulyeon & Choi, Gyunghyun, 2019. "Exploring data envelopment analysis for measuring collaborated innovation efficiency of small and medium-sized enterprises in Korea," European Journal of Operational Research, Elsevier, vol. 278(2), pages 533-545.

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