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Mining Inconsistent Data with Probabilistic Approximations

  • P. G. Clark
  • J. W. Grzymala-BusseEmail author
  • Z. S. Hippe
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 559)

Abstract

Generalized probabilistic approximations, defined using both rough set theory and probability theory, are studied using an approximation space (U, R), where R is an arbitrary binary relation. Generalized probabilistic approximations are applicable in mining inconsistent data (data with conflicting cases) and data with missing attribute values.

Keywords

Probabilistic Approximation Decision Table Approximation Space Rule Induction Indiscernibility Relation 
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 2014

Authors and Affiliations

  • P. G. Clark
    • 1
  • J. W. Grzymala-Busse
    • 1
    • 2
    Email author
  • Z. S. Hippe
    • 3
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of KansasLawrenceUSA
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarszawaPoland
  3. 3.Department of Expert Systems and Artificial IntelligenceUniversity of Information Technology and ManagementRzeszówPoland

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