Uma arquitetura híbrida para descoberta de conhecimento em bases de dados: teoria dos rough sets e redes neurais artificiais mapas auto-organizáveis. / An hybrid architecture for the knowledge discovery in databases: rough sets theory and artificial neural nets self-organizing maps.

AUTOR(ES)
DATA DE PUBLICAÇÃO

2006

RESUMO

Databases of the real world contain a huge amount of data within which several relations are hidden. These relations are difficult to discover by means of traditional methods such as worksheets and operational informative reports. Therefore, the knowledge discovery systems (KDD) appear as a possible solution to extract, from such relations, knowledge to be applied in decision taking. Even using a KDD system, such activity may still continue to be extremely difficult due to the huge amount of data to be processed. Thus, not all data which are part of this base will be useful for a system to discover knowledge. In general, data are usually previously processed before being presented to a knowledge discovery system in order to reduce their quantity and also to select the most relevant data to be used by the system. This research presents the development, application and analysis of an hybrid architecture formed by the combination of the Rough Sets Theory with an artificial neural net architecture named Self-Organizing Maps (SOM) to discover knowledge. The objective is to verify the performance of the hybrid architecture proposed in the generation of clusters in databases. In particular, some of the important performed experiments targeted the decision taking in organizations.

ASSUNTO(S)

redes neurais hybrids systems descoberta de conhecimento neural networks sistemas híbridos knowledge discovery systems

ACESSO AO ARTIGO

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