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Discretization

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Data Preprocessing in Data Mining

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 72))

Abstract

Discretization is an essential preprocessing technique used in many knowledge discovery and data mining tasks. Its main goal is to transform a set of continuous attributes into discrete ones, by associating categorical values to intervals and thus transforming quantitative data into qualitative data. An overview of discretization together with a complete outlook and taxonomy are supplied in Sects. 9.1 and 9.2. We conduct an experimental study in supervised classification involving the most representative discretizers, different types of classifiers, and a large number of data sets (Sect. 9.4).

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Notes

  1. 1.

    http://www.keel.es.

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Authors and Affiliations

  1. Department of Computer Science, University of Jaén, Jaén, Spain

    Salvador García

  2. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Julián Luengo

  3. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Francisco Herrera

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  1. Salvador García
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  2. Julián Luengo
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  3. Francisco Herrera
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Correspondence to Salvador García .

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García, S., Luengo, J., Herrera, F. (2015). Discretization. In: Data Preprocessing in Data Mining. Intelligent Systems Reference Library, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-319-10247-4_9

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  • DOI: https://doi.org/10.1007/978-3-319-10247-4_9

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