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A new data envelopment analysis clustering approach within cross-efficiency framework

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  • Lei Chen
  • Su-Hui Wang
  • Ying-Ming Wang

Abstract

Clustering is used to identify the distribution pattern of the data set based on the similarity of data, but the relationship between data is ignored in the most existing clustering processes. This paper reveals the production relationship between inputs and outputs from the evaluation perspective of decision-making units (DMUs), and innovatively introduces data envelopment analysis cross-efficiency approach to construct a new clustering approach. This new approach not only can cluster DMUs based on the production relationship between data, but also can reflect the preference of decision maker. The clustering results are relatively stable and unique, and they are meaningful for analyzing DMUs in production activities. In addition, the new cross-evaluation strategy based on the nearest neighbor is proposed to further optimize the clustering process by considering data characteristics, and then more reasonable and objectively clustering results can be obtained. Finally, two examples are provided to illustrate the effectiveness and practicability of the new clustering approach.

Suggested Citation

  • Lei Chen & Su-Hui Wang & Ying-Ming Wang, 2022. "A new data envelopment analysis clustering approach within cross-efficiency framework," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(3), pages 664-673, March.
    • Handle: RePEc:taf:tjorxx:v:73:y:2022:i:3:p:664-673
    DOI: 10.1080/01605682.2020.1857667
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    Cited by:

    1. 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.

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