Automated Data Pre-Processing

Static Discretization of Quantitative Attributes
  • Oded Maimon
  • Mark Last
Part of the Massive Computing book series (MACO, volume 1)


As indicated in Chapter 1 above, many learning methods require partition of continuous attributes (features) into discrete intervals. Such methods include neural networks, Bayesian models and standard decision tree algorithms (e.g., 1D3 and C4.5). Since some attributes in real-world databases may be continuous, several methods for discretizing these attributes have been developed.


Mutual Information Continuous Attribute Discretization Method Target Attribute Conditional Mutual Information 
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 Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Oded Maimon
    • 1
  • Mark Last
    • 2
  1. 1.Tel-Aviv UniversityTel-AvivIsrael
  2. 2.University of South FloridaTampaUSA

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