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Introduction to Fuzzy Data Mining Methods

Introduction to Fuzzy Data Mining Methods

Balazs Feil, Janos Abonyi
Copyright: © 2008 |Pages: 41
ISBN13: 9781599048536|ISBN10: 1599048531|EISBN13: 9781599048543
DOI: 10.4018/978-1-59904-853-6.ch003
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MLA

Feil, Balazs, and Janos Abonyi. "Introduction to Fuzzy Data Mining Methods." Handbook of Research on Fuzzy Information Processing in Databases, edited by José Galindo, IGI Global, 2008, pp. 55-95. https://doi.org/10.4018/978-1-59904-853-6.ch003

APA

Feil, B. & Abonyi, J. (2008). Introduction to Fuzzy Data Mining Methods. In J. Galindo (Ed.), Handbook of Research on Fuzzy Information Processing in Databases (pp. 55-95). IGI Global. https://doi.org/10.4018/978-1-59904-853-6.ch003

Chicago

Feil, Balazs, and Janos Abonyi. "Introduction to Fuzzy Data Mining Methods." In Handbook of Research on Fuzzy Information Processing in Databases, edited by José Galindo, 55-95. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-853-6.ch003

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Abstract

This chapter aims to give a comprehensive view about the links between fuzzy logic and data mining. It will be shown that knowledge extracted from simple data sets or huge databases can be represented by fuzzy rule-based expert systems. It is highlighted that both model performance and interpretability of the mined fuzzy models are of major importance, and effort is required to keep the resulting rule bases small and comprehensible. Therefore, in the previous years, soft computing based data mining algorithms have been developed for feature selection, feature extraction, model optimization, and model reduction (rule based simplification). Application of these techniques is illustrated using the wine data classification problem. The results illustrate that fuzzy tools can be applied in a synergistic manner through the nine steps of knowledge discovery.

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