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Data Mining with Neuro-Fuzzy Models

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Data Mining and Computational Intelligence

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 68))

Abstract

Data mining is the central step in a process called knowledge discovery in databases, namely the step in which modeling techniques are applied. Several research areas such as statistics, artificial intelligence, machine learning, and soft computing have contributed to the arsenal of methods for data mining. In this paper, however, we focus on neuro-fuzzy methods for rule learning. In our opinion, fuzzy approaches can play an important role in data mining, because they provide comprehensible results. This goal often seems to be neglected — possibly because comprehensibility is sometimes hard to achieve with other methods.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Magdeburg, Germany

    A. Klose, A. Nürnberger & R. Kruse

  2. British Telecom, Ipswich, UK

    D. Nauck

Authors
  1. A. Klose
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  2. A. Nürnberger
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  3. D. Nauck
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  4. R. Kruse
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Editor information

Editors and Affiliations

  1. Computer Science and Engineering, University of South Florida, 4202 E. Fowler Ave., ENB 118, 33620, Tampa, Florida, USA

    Abraham Kandel

  2. Information Systems Engineering, Ben-Gurion University of the Negev, 84105, Beer-Sheva, Israel

    Mark Last

  3. Department of Computer Science, University of Bern, Neubruckstrasse 10, CH-3012, Bern, Switzerland

    Horst Bunke

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© 2001 Springer-Verlag Berlin Heidelberg

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Klose, A., Nürnberger, A., Nauck, D., Kruse, R. (2001). Data Mining with Neuro-Fuzzy Models. In: Kandel, A., Last, M., Bunke, H. (eds) Data Mining and Computational Intelligence. Studies in Fuzziness and Soft Computing, vol 68. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1825-3_1

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  • DOI: https://doi.org/10.1007/978-3-7908-1825-3_1

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2484-1

  • Online ISBN: 978-3-7908-1825-3

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