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This paper presents a study of optimization of operational recovery credit default with geoprocessing use through geoprocessing tools, developed in the Receivables Management Companhia de Eletricidade do Estado da Bahia-COELBA sector. The work was initially based on the application of Data Mining Tools for Software KNIME 2.9 and later use of the tool of GIS-ArcGIS 10.X/ESRI .Were evaluated and applied analytical processing algorithms, to improve the process of spatial selection and define the best sets logistical credit recovery. The focus study, based on geoprocessing use in cutting action is due to the fact that this process has the largest collection efficiency. It is understood that the efficiency of the cutting action, should the great importance that electricity has on modern life. The research was guided its evolution from analysis of algorithms agglutination and georeferenced database, whose focus was and is acting in the cutting action due to the fact that this process has the largest collection efficiency. For the implementation of the study, through some geoprocessing techniques, the Mpar-cluster, this optimization model was developed from a custom algorithm for spatial selection, that had with subsidy: information stored in geographic databases, database information alphanumeric, images (aerial photographs and satellite images), text files, and digital tables.

Mpar-Cluster: Applied Algorithm of Geo-Selection for Optimization of the Credit Recovery of Electricity Supply