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Data Mining and Knowledge Discovery Handbook pp 37–57Cite as

Handling Missing Attribute Values

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Abstract

In this chapter methods of handling missing attribute values in Data Mining are described. These methods are categorized into sequential and parallel. In sequential methods, missing attribute values are replaced by known values first, as a preprocessing, then the knowledge is acquired for a data set with all known attribute values. In parallel methods, there is no preprocessing, i.e., knowledge is acquired directly from the original data sets. In this chapter the main emphasis is put on rule induction. Methods of handling attribute values for decision tree generation are only briefly summarized.

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Author information

Authors and Affiliations

  1. University of Kansas, USA

    Jerzy W. Grzymala-Busse

  2. FilterLogix, USA

    Witold J. Grzymala-Busse

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  1. Jerzy W. Grzymala-Busse
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  2. Witold J. Grzymala-Busse
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Editor information

Editors and Affiliations

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

    Oded Maimon  & Lior Rokach  & 

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Grzymala-Busse, J.W., Grzymala-Busse, W.J. (2005). Handling Missing Attribute Values. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/0-387-25465-X_3

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  • DOI: https://doi.org/10.1007/0-387-25465-X_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-24435-8

  • Online ISBN: 978-0-387-25465-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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