Proposta de Método para Redução do Conjunto de Regras de Associação Resultantes do Algoritmo Apriori

Abstract

The use of association rules algorithms within data mining is recognized as being of great value in the search for knowledge about databases. Very often the number of rules generated is high, sometimes even in databases with small volume, so the success in the analysis of results can be hampered by this quantitative. The purpose of this research is to present a method for reducing the quantitative of rules generated with association algorithms. For this, a computational algorithm was developed with the use of a Weka API, which allows the execution of the method on different types of databases. After the development, tests were carried out on three types of databases: synthetic, model and real. Efficient results were obtained in reducing the number of rules, where the worst case presented a gain of more than 50%, considering the concepts of support, confidence and lift as measures. This study concluded that the proposed model is feasible and quite interesting, contributing to the analysis of the results of association rules generated from the use of algorithms.