MINERAÇÃO DE DADOS APLICADA À CLASSIFICAÇÃO DOS CONTRIBUINTES DE ICMS DA SEFAZ-GO

Abstract

With the exponential increase in the volume of data stored and the high potential for hidden knowledge in these data that can aid in the strategies and decision making of organizations, much has been invested in information technology and telecommunication. The purpose of this dissertation was to apply the Knowledge Discovery in Database (DCBD) process in order to classify the taxpayers of SEFAZ-GO ICMS in High Eviction and Low Eviction, through the task of data mining Supervised Classification, Implemented by the algorithm J48, on the WEKA computing platform. Three experiments were carried out with a sample of ICMS taxpayers data from the wholesale sector of the city of Goiânia-GO, with attributes selected from the Tax Code of the State of Goiás. During the experiments, the AttributeSelection and Discretize algorithms were applied. Reduction of attributes and transformation of the continuous variables into discrete ones, respectively. The statistical indices Confusion Matrix and Kappa Coefficient were used as validation metrics of the proposed model. After each experiment, classification rules were extracted, thus forming the proposed predictive model of classification. In the best scenario, a correct classification rate of 84% accuracy was obtained. Data mining is a reality within many organizations and can be a strong ally in fulfilling the, trivial, task of knowledge discovery in corporate databases.