Increasing the discriminatory power of DEA in the presence of the sample heterogeneity with cluster analysis and decision trees.
Sergey SamoilenkoKweku-Muata Osei-BrysonPublished in: Expert Syst. Appl. (2008)
Keyphrases
- cluster analysis
- decision trees
- discriminatory power
- categorical data
- data clustering
- clustering method
- data mining
- data analysis
- hierarchical latent class models
- k means
- factor analysis
- data envelopment analysis
- recognition rate
- clustering algorithm
- data mining techniques
- clustering analysis
- feature selection
- cluster validity
- correlation analysis
- feature space
- fuzzy c means
- sample size
- fuzzy clustering
- discriminant analysis
- database
- support vector
- training data
- machine learning
- partitional clustering
- databases