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Data Mining with Graphical Models

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Book cover Discovery Science (DS 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2534))

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Abstract

Data Mining, or Knowledge Discovery in Databases, is a fairly young research area that has emerged as a reply to the flood of data we are faced with nowadays. It tries to meet the challenge to develop methods that can help human beings to discover useful patterns in their data. One of these techniques - and definitely one of the most important, because it can be used for such frequent data mining tasks like classifier construction and dependence analysis - is learning graphical models from datasets of sample cases. In this paper we review the ideas underlying graphical models, with a special emphasis on the less well known possibilistic networks. We discuss the main principles of learning graphical models from data and consider briefly some algorithms that have been proposed for this task as well as data preprocessing methods and evaluation measures.

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Author information

Authors and Affiliations

  1. Department of Knowledge Processing and Language Engineering, Otto-von-Guericke-University of Magdeburg, Universitätsplatz 2, D-39106, Magdeburg, Germany

    Rudolf Kruse & Christian Borgelt

Authors
  1. Rudolf Kruse
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  2. Christian Borgelt
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Editor information

Editors and Affiliations

  1. Deutsches Forschungszentrum für Künstliche Intelligenz, Stuhlsatzenhausweg 3, 66123, Saarbrücken, Germany

    Steffen Lange

  2. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430, Tokyo, Japan

    Ken Satoh

  3. Department of Computer Science, University of Maryland, College Park, 20742, Maryland, MD, USA

    Carl H. Smith

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© 2002 Springer-Verlag Berlin Heidelberg

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Kruse, R., Borgelt, C. (2002). Data Mining with Graphical Models. In: Lange, S., Satoh, K., Smith, C.H. (eds) Discovery Science. DS 2002. Lecture Notes in Computer Science, vol 2534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36182-0_2

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  • DOI: https://doi.org/10.1007/3-540-36182-0_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00188-1

  • Online ISBN: 978-3-540-36182-4

  • eBook Packages: Springer Book Archive

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