A Methodology Using Neural Network to Cluster Validity Discovered from a Marketing Database

The databases of real world contains a huge volume of data and among them there are hidden piles of interesting relations that are actually very hard to find out. The knowledge discovery databases (KDD) appear as a possible solution to find out such relations aiming at converting information into knowledge. However, not a data presented in the bases are useful to a KDD. Usually, data are processed before being presented to a KDD aiming at reducing the amount of data and also at selecting more relevant data to be used by the system. The purpose of this paper is to describe a validation methodology, through of a MLP neural network, to the knowledge discovered by a hybrid architecture composed by rough sets theory used to pre-processing the data to be presented to self-organizing maps neural network, which data cluster.