Data mining tasks and methods: Classification: neural network approaches

Authors:
Andreas Nürnberger;Witold Pedrycz;Rudolf Kruse
Affiliations:
BT Research Fellow, University of California, Berkeley;Professor of Computer and Electrical Engineering, University of Alberta, Edmonton, Canada;Professor of Computer Science, Otto-von-Guericke-University, Magdeburg, Germany
Venue:
Handbook of data mining and knowledge discovery
Year:
2002

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Abstract

This article elaborates on the role of neural networks in data mining, especially classification, and presents various ways of using them in this area. In order to do this the main architectures of neural networks (including multilayer perceptrons and radial basis function networks) are reviewed, and an overview of the classification process and the training of neural networks is given. Furthermore, the interpretation of neural networks and the generation of rules based on already trained networks are discussed and exemplified on a number of rule extraction algorithms. Finally, the role of neuro-fuzzy systems in the process of designing interpretable neural networks is described.