Fuzzy Neural Network Models for Knowledge Discovery

Fuzzy Neural Network Models for Knowledge Discovery

Arun Kulkarni (The University of Texas at Tyler, USA) and Sara McCaslin (The University of Texas at Tyler, USA)
DOI: 10.4018/978-1-59904-982-3.ch006
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Abstract

This chapter introduces fuzzy neural network models as means for knowledge discovery from databases. It describes architectures and learning algorithms for fuzzy neural networks. In addition, it introduces an algorithm for extracting and optimizing classification rules from a trained fuzzy neural network. As an illustration, multispectral satellite images have been analyzed using fuzzy neural network models. The authors hope that fuzzy neural network models and the methodology for generating classification rules from data samples provide a valuable tool for knowledge discovery. The algorithms are useful in a variety of data mining applications such as environment change detection, military reconnaissance, crop yield prediction, financial crimes and money laundering, and insurance fraud detection.
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Background

The process of knowledge discovery from databases (KDD) is outlined in Figure 1. This process is described in the following sections.

Figure 1.

Knowledge discovery from databases (KDD) process

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