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Uncertainty Handling in Data Mining

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Uncertainty Handling and Quality Assessment in Data Mining

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Abstract

The KDD process aims at searching for interesting patterns in large real-world data sets. The representation of the extracted knowledge may have various forms, depending on the specific data mining technique used, such as classification, association rules, clustering, etc.

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Author information

Authors and Affiliations

  1. Department of Informatics, Athens University of Economics and Business, Greece

    Michalis Vazirgiannis PhD & Maria Halkidi MSc

  2. Department of Computer Science and Engineering, University of California, Riverside, USA

    Dimitrios Gunopulos PhD

Authors
  1. Michalis Vazirgiannis PhD
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  2. Maria Halkidi MSc
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  3. Dimitrios Gunopulos PhD
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© 2003 Springer-Verlag London

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Vazirgiannis, M., Halkidi, M., Gunopulos, D. (2003). Uncertainty Handling in Data Mining. In: Uncertainty Handling and Quality Assessment in Data Mining. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-4471-0031-7_4

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  • DOI: https://doi.org/10.1007/978-1-4471-0031-7_4

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-1119-1

  • Online ISBN: 978-1-4471-0031-7

  • eBook Packages: Springer Book Archive

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