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Rough Set Approach to Incomplete Data

  • Jerzy W. Grzymala-Busse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3070)

Abstract

In this paper incomplete data are assumed to be decision tables with missing attribute values. We discuss two main cases of missing attribute values: lost values (a value was recorded but it is unavailable) and “do not care” conditions (the original values were irrelevant). Through the entire paper the same calculus, based on computations of blocks of attribute-value pairs, is used. Complete data are characterized by the indiscernibility relations, a basic idea of rough set theory. Incomplete data are characterized by characteristic relations. Using characteristic relations, lower and upper approximations are generalized for incomplete data. Finally, from three definitions of such approximations certain and possible rule sets may be induced.

Keywords

Incomplete Data Characteristic Relation Decision Table Rule Induction Entire Paper 
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 2004

Authors and Affiliations

  • Jerzy W. Grzymala-Busse
    • 1
    • 2
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of KansasLawrenceUSA
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

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