A controlled data envelopment analysis clustering approach based on individual perspective

Abstract

Unlike most existing clustering methods, data envelopment analysis (DEA) clusters decision-making units (DMUs) based on production characteristics rather than distance. The clustering results obtained using the DEA clustering approach reflect the production relationship between the inputs and outputs of the DMUs to better identify the inherent production correlation between them. However, existing DEA-based clustering approaches struggle to rationally assign unique clusters to DMUs that exhibit multiple production characteristics and lack the further processing of clustering results. Therefore, this study proposes a new DEA clustering approach based on the individual perspective of DMUs that incorporates prospect theory to reflect the individual preferences of DMUs to assign each DMU to a relatively unique cluster. Furthermore, a clustering adjustment method and a clustering reduction method are proposed to further improve the clustering quality. The former can handle some special clusters according to the decision-maker’s preference, and the latter permits the realization of an arbitrary number of clusters. The new DEA clustering approach is more reliable and flexible, and more valuable information can be provided for decision-makers. Finally, the validity of the new approach is verified through a comparison with existing approaches in two numerical cases, and an empirical example is used to illustrate its practicability.