Fuzzy Neural Network Models for Knowledge Discovery

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Author(s): Arun Kulkarni (The University of Texas at Tyler, USA)and Sara McCaslin (The University of Texas at Tyler, USA)
Copyright: 2009
Pages: 17
Source title: Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery
Source Author(s)/Editor(s): Hsiao-Fan Wang (National Tsing Hua University, ROC)
DOI: 10.4018/978-1-59904-982-3.ch006

Keywords: Data Mining and Databases / Information Science Reference / Knowledge Discovery / Library & Information Science

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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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