DEA implementation and clustering analysis using the K-Means algorithm

Nowadays, problems that involve efficiency analysis and decision support systems inside a company need special attention and a number of tools have been developed to support managers. DEA - Data Envelopment Analysis is one of these tools and its use is increasing in research and in new developments. The problem is how to improve the quality of DEA analysis when the DNM (decision-making unit) it analyzes is considered efficient, and how to guarantee the analysis if the input and output parameters that contain a lot of zeros? Probably these parameters have not been considered in how to visualize the inputs and outputs in n-dimensional space? This paper proposes combining another tool with DEA based in data mining, CLUSTERING, to evaluate the efficiency analyses made for DEA tools, and visualize groups which have inefficient DMUs, based on the K-Means algorithm, and apply over a telecommunication database that contains an indicator of efficiency of the telephone installation in the Brazilian market.