http://www.yuhuaqian.net/Cms_Data/Contents/SXU_YHQ/Folders/JournalPapers/~contents/KD22X6U6AUY584JT/Positive%20approximation-an%20accelerator%20for%20attribute%20reduction%20in%20rough%20set%20theory.pdf

Positive approximation: An accelerator for attribute reduction in rough set theory

Attribute reduction in decision-theoretic rough set models

Granular computing and knowledge reduction are two basic issues in knowledge representation and data mining. Granular structure of concept lattices with application in knowledge reduction in formal concept analysis is examined in this paper. Information granules and their properties in a formal context are first discussed. Concepts of a granular consistent set and a granular reduct in the formal context are then introduced. Discernibility matrices and Boolean functions are, respectively, employed to determine granular consistent sets and calculate granular reducts in formal contexts. Methods of knowledge reduction in a consistent formal decision context are also explored. Finally, knowledge hidden in such a context is unraveled in the form of compact implication rules.

Granular Computing and Knowledge Reduction in Formal Contexts

http://www2.cs.uregina.ca/~yyao/PAPERS/is_reduct_construction.pdf

Mei Zhang

Papers

Knowledge reduction in random information systems via Dempster-Shafer theory of evidence