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Knowledge Reduction Based on Granular Computing from Decision Information Systems

  • Lin Sun
  • Jiucheng Xu
  • Shuangqun Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6401)

Abstract

Efficient knowledge reduction in large inconsistent decision information systems is a challenging problem. Moreover, existing approaches have still their own limitations. To address these problems, in this article, by applying the technique of granular computing, provided some rigorous and detailed proofs, and discussed the relationship between granular reduct introduced and knowledge reduction based on positive region related to simplicity decision information systems. By using radix sorting and hash methods, the object granules as basic processing elements were employed to investigate knowledge reduction. The proposed method can be applied to both consistent and inconsistent decision information systems.

Keywords

Granular computing Rough set theory Knowledge reduction Decision information systems Granular 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Lin Sun
    • 1
  • Jiucheng Xu
    • 1
  • Shuangqun Li
    • 1
  1. 1.College of Computer and Information TechnologyHenan Normal UniversityXinxiang HenanChina

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