Summary of literature on clustering analysis used in combination with DEA
| Authors | Context of analysis | Clustering method | Clustering variables | Nr. of DMUs | Nr of clusters | Method of combination with DEA |
|---|---|---|---|---|---|---|
| Samoilenco and Esei-Bryson (2008, 2010) | Countries in transition from centralized economies to market economies | Two-step approach based on k-means | Levels of DMUs inputs and outputs | 18 | 2 clusters, through user-specified threshold and domain expert knowledge | Clustering is adopted to DEA results on the entire dataset |
| Sharma and Yu (2009) | Container terminals | Kohonen's Self Organizing Map (KSOM) preceded by a stratifying method | Levels of DMUs inputs | 70 | 4 clusters through unsupervised clustering | Clustering is adopted to DEA results on the entire dataset |
| Amirteimoori and Kordrostami (2013) | Retail: Bank branches | Original method based on branches size | Size | 64 | 3 clusters by domain expert knowledge | Clustering is adopted prior to DEA. DEA is used within clusters |
| Hajiagha et al. (2016) | Retail: Bank branches | Fuzzy c-means clustering | Levels of DMUs inputs and outputs | 117 | 2 clusters set by the analyst | Clustering is adopted prior to DEA. DEA is used within clusters |
| Li et al. (2016) | Retail: Gas stations | Ward's hierarchical clustering | Levels of DMUs inputs and outputs | 197 | 4 clusters set by the analyst | Clustering is adopted to DEA results on the entire dataset |
| Omrani et al. (2018) | Hospitals | Fuzzy c-means clustering | Environmental characteristics (population; GDP per capita) | 288 | 5 clusters set by the analyst | Clustering is adopted prior to DEA. DEA is used within clusters |
| Costa et al. (2019) | Electricity energy distribution utilities | Spatial Bayesian clustering | Spatial location (assuming that geographically closer DMUs are homogeneous) | 64 | 2 clusters as result of the analysis | Clustering is adopted to DEA results on the entire dataset |
| Samoilenko and Osei-Bryson (2019) | Sub-Saharan African countries | Hybrid partitional/hierarchical approach | Economic development; socioeconomic impact of ICT; growth in productivity | 27 | 3 clusters by domain expert knowledge | Clustering is adopted to DEA results on the entire dataset |
| Cinaroglu (2020) | Hospitals | K-means clustering | regional areas are clustered on welfare indicators | 81 | 5 clusters through a combination of different factors | Clustering is adopted prior to DEA. DEA is used within clusters |
| Zarrin et al. (2022) | Hospitals | Self-Organizing Map-Artificial Neural Network | hospital's characteristics | 1,124 | 3 clusters through unsupervised clustering | Clustering is adopted prior to DEA. DEA is used within clusters |
| Tsionas (2023) | Commercial banks | Convex non-parametric least squares | Commercial banks operating variables nd technical feasibility | 285 | 3 clusters through Bayesian Model Averaging | DEA is used on the entire dataset. Clustering is adopted to DEA results |
| Our contribution | Retail: Kitchen furniture | Factorial techniques (PCA and MCA) on Big Data + Combined Agglomerative Hierarchical Clustering and k-means algorithm method | Structural characteristics of stores and socio-demographic characteristics of their catchment area | 541 | 3 clusters as result of an agglomerative hierarchical clustering, based on factorial scores | Clustering is adopted prior to DEA. DEA is used within clusters |
| Authors | Context of analysis | Clustering method | Clustering variables | Nr. of DMUs | Nr of clusters | Method of combination with DEA |
|---|---|---|---|---|---|---|
| Countries in transition from centralized economies to market economies | Two-step approach based on k-means | Levels of DMUs inputs and outputs | 18 | 2 clusters, through user-specified threshold and domain expert knowledge | Clustering is adopted to DEA results on the entire dataset | |
| Container terminals | Kohonen's Self Organizing Map (KSOM) preceded by a stratifying method | Levels of DMUs inputs | 70 | 4 clusters through unsupervised clustering | Clustering is adopted to DEA results on the entire dataset | |
| Retail: Bank branches | Original method based on branches size | Size | 64 | 3 clusters by domain expert knowledge | Clustering is adopted prior to DEA. DEA is used within clusters | |
| Retail: Bank branches | Fuzzy c-means clustering | Levels of DMUs inputs and outputs | 117 | 2 clusters set by the analyst | Clustering is adopted prior to DEA. DEA is used within clusters | |
| Retail: Gas stations | Ward's hierarchical clustering | Levels of DMUs inputs and outputs | 197 | 4 clusters set by the analyst | Clustering is adopted to DEA results on the entire dataset | |
| Hospitals | Fuzzy c-means clustering | Environmental characteristics (population; GDP per capita) | 288 | 5 clusters set by the analyst | Clustering is adopted prior to DEA. DEA is used within clusters | |
| Electricity energy distribution utilities | Spatial Bayesian clustering | Spatial location (assuming that geographically closer DMUs are homogeneous) | 64 | 2 clusters as result of the analysis | Clustering is adopted to DEA results on the entire dataset | |
| Sub-Saharan African countries | Hybrid partitional/hierarchical approach | Economic development; socioeconomic impact of ICT; growth in productivity | 27 | 3 clusters by domain expert knowledge | Clustering is adopted to DEA results on the entire dataset | |
| Hospitals | K-means clustering | regional areas are clustered on welfare indicators | 81 | 5 clusters through a combination of different factors | Clustering is adopted prior to DEA. DEA is used within clusters | |
| Hospitals | Self-Organizing Map-Artificial Neural Network | hospital's characteristics | 1,124 | 3 clusters through unsupervised clustering | Clustering is adopted prior to DEA. DEA is used within clusters | |
| Commercial banks | Convex non-parametric least squares | Commercial banks operating variables nd technical feasibility | 285 | 3 clusters through Bayesian Model Averaging | DEA is used on the entire dataset. Clustering is adopted to DEA results | |
| Our contribution | Retail: Kitchen furniture | Factorial techniques (PCA and MCA) on Big Data + Combined Agglomerative Hierarchical Clustering and k-means algorithm method | Structural characteristics of stores and socio-demographic characteristics of their catchment area | 541 | 3 clusters as result of an agglomerative hierarchical clustering, based on factorial scores | Clustering is adopted prior to DEA. DEA is used within clusters |