DEA Implementation And Clustering Analysis Using The K-Means Algorithm
Free (open access)
C. A. A. Lemos, M. P. E. Lins & N. F. F. Ebecken
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 DMU (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. Keywords: Data Envelopment Analysis, clustering, data mining, telecommunication quality indicator, decision support system. 1 Introduction Problems that involve efficiency analysis inside a company need to have special attention. Tools are being development to support managers. Some companies use complex formulations based on traditional statistical methods and others are using new environments based on computational intelligence and others tools. DEA  is one of these tools that obtain relative efficiency between two or more companies, departments or groups. The problem in DEA is how to improve the quality of analysis when the DMU (decision-making unit) it analyzes is
Data Envelopment Analysis, clustering, data mining, telecommunication quality indicator, decision support system.