Data Reduction

  • Salvador GarcíaEmail author
  • Julián Luengo
  • Francisco Herrera
Part of the Intelligent Systems Reference Library book series (ISRL, volume 72)


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.


Principal Component Analysis Empirical Likelihood Locally Linear Embedding Data Reduction Technique Brute Force Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Salvador García
    • 1
    Email author
  • Julián Luengo
    • 2
  • Francisco Herrera
    • 3
  1. 1.Department of Computer ScienceUniversity of JaénJaénSpain
  2. 2.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  3. 3.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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