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Data Mining and Knowledge Discovery Handbook pp 249–265Cite as

Rule Induction

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Summary

This chapter begins with a brief discussion of some problems associated with input data. Then different rule types are defined. Three representative rule induction methods: LEM1, LEM2, and AQ are presented. An idea of a classification system, where rule sets are utilized to classify new cases, is introduced. Methods to evaluate an error rate associated with classification of unseen cases using the rule set are described. Finally, some more advanced methods are listed.

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Author information

Authors and Affiliations

  1. University of Kansas, Lawrence, Kansas, 66045, USA

    Jerzy W. Grzymala-Busse

Authors
  1. Jerzy W. Grzymala-Busse
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Editor information

Editors and Affiliations

  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

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

    Lior Rokach

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© 2009 Springer Science+Business Media, LLC

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Grzymala-Busse, J.W. (2009). Rule Induction. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_13

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  • DOI: https://doi.org/10.1007/978-0-387-09823-4_13

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-09822-7

  • Online ISBN: 978-0-387-09823-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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