Kaishe Qu

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This paper introduces granular computing (GrC) into formal concept analysis (FCA). It provides a unified model for concept lattice building and rule extraction on a fuzzy granularity base for different granulations. One of the strengths of GrC is that larger granulations help to hide some specific details, whereas FCA in a GrC context can prevent losses due(More)
Keywords: FCA Rough set theory Rough concept lattice Rugh concept Decision dependency a b s t r a c t This paper proposes a rough set model based on formal concept analysis. In this model, a solution to an algebraic structure problem is first provided in an information system: a lattice structure is inferred from the information system and corresponding(More)
a r t i c l e i n f o a b s t r a c t Due to its special role on logical deduction and practical applications of attribute implications, canonical basis has attracted much attention and been widely studied in Formal Concept Analysis. Canonical basis is constructed on pseudo-intents and, as an attribute implication basis, possesses of many important(More)
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