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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, and 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, Lawrence, KS, USA

    Jerzy W. Grzymala-Busse

  2. TouchNet Information Systems, Inc., Lenexa, KS, USA

    Witold J. Grzymala-Busse

Authors
  1. Jerzy W. Grzymala-Busse
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  2. Witold J. Grzymala-Busse
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Corresponding author

Correspondence to Jerzy W. Grzymala-Busse .

Editor information

Editors and Affiliations

  1. Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  2. Department of Industrial Engineering, Tel Aviv University, Ramat Aviv, Israel

    Oded Maimon

  3. Department of Industrial Engineering, Tel Aviv University, Tel Aviv, Israel

    Erez Shmueli

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Grzymala-Busse, J.W., Grzymala-Busse, W.J. (2023). Handling Missing Attribute Values. In: Rokach, L., Maimon, O., Shmueli, E. (eds) Machine Learning for Data Science Handbook. Springer, Cham. https://doi.org/10.1007/978-3-031-24628-9_2

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