Algorithms for clustering data
Algorithms for clustering data
Academic departments efficiency via DEA
Computers and Operations Research
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Stochastic frontier analysis
Data Envelopment Analysis: Theory, Methodology and Application
Data Envelopment Analysis: Theory, Methodology and Application
Modern Information Retrieval
Cluster validity methods: part I
ACM SIGMOD Record
Clustering validity checking methods: part II
ACM SIGMOD Record
Applying Data Mining Techniques to a Health Insurance Information System
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Combining inductive and deductive tools for data analysis
AI Communications
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We propose a novel approach that combines data mining and linear programming techniques for classifying organizational units, such as research centers. We show how our proposal of clustering organizational units based on both efficiency and input/output parameters turns out to be effective in identifying groups of similar organizational units. We also propose the replacement of an expensive efficiency measurement, based on the solution of linear programs, with a simple but more efficient formula to be exploited in the clustering process. Preliminary experimental results, obtained from an analysis of research centers in the agro-food sector, show the effectiveness of our approach.