Knowledge Discovery in Databases - KDD - is a process that consists of several steps, beginning with the collection of data for the problem under analysis and ending with the interpretation and evaluation of the final results. This paper discusses the influence of exploratory data analysis on the performance of Data Mining techniques with respect to the classification of new patterns, based on its application to a medical problem, and compares the performance of these techniques in order to identify the one with the highest percentage of successes. The results of this study lead to the conclusion that, providing this analysis is done properly, it can significantly improve the performance of these techniques and serve as an important tool to optimize the end results. For the problem under study, the techniques involving a Linear Programming model and Neural Networks were the ones showing the lowest percentages of errors for the test sets, presenting good generalization capacities.
data mining; KDD process; exploratory data analysis
