A review of genetic-fuzzy approaches to knowledge discovery in databases

Authors

  • Wesley Romão UEM
  • Alex Alves Freitas PUC-PR
  • Roberto dos Santos Pacheco UFSC

DOI:

https://doi.org/10.4025/actascitechnol.v22i0.3074

Keywords:

algoritmos genéticos, conjuntos difusos, descoberta de conhecimento

Abstract

Knowledge Discovery in Databases (KDD) process consists of many stages among which the main one is Data Mining (DM). There are many DM tasks but the discovery of classification rules is the most known. The classification task can be addressed by conventional algorithms (e.g., statistics) or by artificial intelligence techniques (e.g., neural networks, evolutionary algorithms, etc.). In this research we are interested in investigating the applicability of a hybrid combination of genetic algorithms and fuzzy sets to find rules in large and complex spaces. This paper reviews some hybrid Genetic-Fuzzy approaches for the extraction of classification rules found in the literature and discusses the possibility of adapting them to knowledge discovery in Science and Technology (S&T) databases

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Author Biography

Wesley Romão, UEM

Possui graduação pela Universidade Estadual de Maringá (1984) , especialização em Fundamentos da Informática pela Universidade Estadual de Maringá (1990) , mestrado em Ciências da Computação pela Universidade Federal de Santa Catarina (1996) e doutorado em Engenharia de Produção pela Universidade Federal de Santa Catarina (2002) . Atualmente é Professor Adjunto da Universidade Estadual de Maringá. Tem experiência na área de Ciência da Computação , com ênfase em Sistemas de Informação. Atuando principalmente nos seguintes temas: Descoberta de Conhecimento, Mineração de Dados, Algoritmos Genéticos, banco de dados, Sistemas de Gestão em Ciência e Tecnologia Currí­culo Lattes

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Published

2008-05-13

How to Cite

Romão, W., Freitas, A. A., & Pacheco, R. dos S. (2008). A review of genetic-fuzzy approaches to knowledge discovery in databases. Acta Scientiarum. Technology, 22, 1347–1359. https://doi.org/10.4025/actascitechnol.v22i0.3074

Issue

Vol. 22 (2000)

Section

Computer Science

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