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Incomplete Data and Generalization of Indiscernibility Relation, Definability, and Approximations

  • Jerzy W. Grzymala-Busse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3641)

Abstract

In incomplete data missing attribute values may be universally interpreted in several ways. Four approaches to missing attribute values are discussed in this paper: lost values, ”do not care” conditions, restricted ”do not care” conditions, and attribute-concept values. Rough set ideas, such as attribute-value pair blocks, characteristic sets, characteristic relations and generalization of lower and upper approximations are used in these four approaches. A generalized rough set methodology, achieved in the process, may be used for other applications as well. Additionally, this generalized methodology is compared with other extensions of rough set concepts.

Keywords

Incomplete Data Characteristic Relation Decision Table 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-Verlag Berlin Heidelberg 2005

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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