Practical Approximation of Optimal Multivariate Discretization

  • Tapio Elomaa
  • Jussi Kujala
  • Juho Rousu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


Discretization of the value range of a numerical feature is a common task in data mining and machine learning. Optimal multivariate discretization is in general computationally intractable. We have proposed approximation algorithms with performance guarantees for training error minimization by axis-parallel hyperplanes. This work studies their efficiency and practicability. We give efficient implementations to both greedy set covering and linear programming approximation of optimal multivariate discretization. We also contrast the algorithms empirically to an efficient heuristic discretization method.


Bayesian Network Association Rule Minority Class Linear Program Relaxation Performance Guarantee 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tapio Elomaa
    • 1
  • Jussi Kujala
    • 1
  • Juho Rousu
    • 2
  1. 1.Institute of Software SystemsTampere University of TechnologyFinland
  2. 2.Department of Computer ScienceUniversity of HelsinkiFinland

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