Clustering-Based Method for Data Envelopment Analysis

Data Envelopment Analysis (DEA) is a powerful performance measurement in economic sector and operations research to assess the relative efficiency for each decision making unit (DMU). In general, there are two assumptions in DEA. Firstly, the DEA assumes that all DMUs are homogenous in their environments and secondly, the DEA is a deterministic approach which refers to not allow to noise or errors in measurements. A large number of papers have addressed the DEA models but not many of them have focused on the heterogonous of DMUs and on the scalability over large datasets (i.e. when datasets contain a large number of DMUs). In this paper, we propose a new method for determining efficiently the performance scores of non-homogenous DMUs based on clustering methods to discover the outliers early. Experimental results presented show big improvements for our approach in assessing a funding transportation system for school districts in North Dakota State.