Abstract
Granular reduction has been of great interests for formal context analysis. From the perspective of granular computing, granular matrices are proposed to represent the extensions and intensions of formal concepts. Within this framework, irreducible elements are studied. Furthermore, similarity degree, information granular and information entropy are developed to specify the significance of attribute. In this case, heuristic approaches for granular reduct are proposed. Finally, several data sets are experimented to demonstrate the feasibility and efficiency of our method. Our methods present a new framework for granular reduction in formal concept analysis.
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