Granular Computing and Knowledge Reduction in Formal Contexts

Authors:
Wei-Zhi Wu;Yee Leung;Ju-Sheng Mi
Affiliations:
Zhejiang Ocean University, Zhoushan;The Chinese University of Hong Kong, Hong Kong;Hebei Normal University, Shijiazhuang
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
2009

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Abstract

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.