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A Rough Set Approach to Knowledge Discovery by Relation Approximation

  • Sinh Hoa Nguyen
  • Hung Son Nguyen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 297)

Abstract

Relations play a crucial role in rough set theory and KDD. Typical applications of rough set methods in KDD are based on a given relation or a given family of relations (e.g. indiscernibility relation, tolerance relation, etc.). In this paper we propose a general framework for concept discovery from data using two-level learning approach. The first level responds for approximation of an unknown or a partially defined relation, and the second level uses the approximated relation to approximate some more complex concepts. We consider two types of relations including similarity and preference relations. We discuss the rough-set based methods for relation discovery and present experimental results for some complex data sets.

Keywords

Relation Approximation Decision Table Ranking List Target Concept Ranking Algorithm 
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 2012

Authors and Affiliations

  • Sinh Hoa Nguyen
    • 1
    • 2
  • Hung Son Nguyen
    • 1
  1. 1.Institute of MathematicsWarsaw UniversityWarsawPoland
  2. 2.Polish-Japanese Institute of Information TechnologyWarszawaPoland

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