CONTECSI - International Conference on Information Systems and Technology Management - ISSN 2448-1041, 5º CONTECSI - International Conference on Information Systems and Technology Management

Tamanho da fonte: 
VALIDAÇÃO DO CONHECIMENTO DESCOBERTO PELA ARQUITETURA HÍBRIDA (TEORIA DOS ROUGH SETS E REDE SELF-ORGANIZING MAPS) ATRAVÉS DE UMA REDE MULTILAYER PERCEPTRONS USANDO UMA BASE DE DADOS DE CONSUMIDORES
Renato José Sassi, Emilio Del Moral Hernandez, Leandro Augusto da Silva

Última alteração: 2014-10-29

Resumo


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 all 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 discovery 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. The
experimental results motivate the use of Hybrid Architecture in
knowledge discovery databases.

Palavras-chave


Hybrids Systems; Neural Network; Knowledge Discovery Systems; Rough Sets; Self-Organizing Maps; Multilayer Perceptrons.