Weights based clustering in Data Envelopment Analysis using Kohonen Neural Network: An Application in Brazilian Electrical Sector

This paper presents a methodology developed using the techniques Data Envelopment Analysis (DEA) and Self-Organizing Maps (SOM) in order to cluster productive units under analysis. In this study, the input vectors are the inputs and outputs contributions from DEA in order to generate groups with similar profiles considering the relevance of selected variables. This way, this clustering is different from most part of the applications found in literature, which commonly use the efficiency scores assessed by DEA as input vector. For this purpose, two processes are incorporated into the methodology to apply the method: the weights used are converted into the contribution of each variable to the DMU and, in addition, a problem of linear programming is used to determine which set of weights from the optimal weights generated by DEA will be used as input vector of SOM.