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Granular matrix method of attribute reduction in formal contexts

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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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Acknowledgements

This work is supported by Grants from the National Natural Science Foundation of China (Nos. 11871259, 11701258 and 61379021 ).

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Authors and Affiliations

  1. School of Mathematical Sciences, Xiamen University, Xiamen, 361005, China

    Yidong Lin

  2. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, 363000, China

    Yidong Lin & Jinjin Li

  3. Georgetown University, Washington, DC, 20057, USA

    Hongkun Wang

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  1. Yidong Lin
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  2. Jinjin Li
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  3. Hongkun Wang
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Correspondence to Jinjin Li.

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Communicated by V. Loia.

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Lin, Y., Li, J. & Wang, H. Granular matrix method of attribute reduction in formal contexts. Soft Comput 24, 16303–16314 (2020). https://doi.org/10.1007/s00500-020-04941-5

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