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Data Reduction

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Data Preprocessing in Data Mining

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

Abstract

The most common tasks for data reduction carried out in Data Mining consist of removing or grouping the data through the two main dimensions, examples and attributes; and simplifying the domain of the data. A global overview to this respect is given in Sect. 6.1. One of the well-known problems in Data Mining is the “curse of dimensionality”, related with the usual high amount of attributes in data. Section 6.2 deals with this problem. Data sampling and data simplification are introduced in Sects. 6.3 and 6.4, respectively, providing the basic notions on these topics for further analysis and explanation in subsequent chapters of the book.

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Author information

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

Authors
  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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© 2015 Springer International Publishing Switzerland

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García, S., Luengo, J., Herrera, F. (2015). Data Reduction. In: Data Preprocessing in Data Mining. Intelligent Systems Reference Library, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-319-10247-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-10247-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10246-7

  • Online ISBN: 978-3-319-10247-4

  • eBook Packages: EngineeringEngineering (R0)

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