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Soft Computing
Erschienen in: Soft Computing 21/2020

28.04.2020 | Methodologies and Application

Granular matrix method of attribute reduction in formal contexts

verfasst von: Yidong Lin, Jinjin Li, Hongkun Wang

Erschienen in: Soft Computing | Ausgabe 21/2020

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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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Metadaten
Titel
Granular matrix method of attribute reduction in formal contexts
verfasst von
Yidong Lin
Jinjin Li
Hongkun Wang
Publikationsdatum
28.04.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 21/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-020-04941-5

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