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0–1 linear integer programming method for granule knowledge reduction and attribute reduction in concept lattices

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Abstract

Knowledge reduction is one of the key issues in formal concept analysis, and there have been many studies on this topic. Granule knowledge reduction and attribute reduction are two of the most important knowledge reduction in formal concept analysis. Firstly, theorem to character granule knowledge reduction is given, and granule knowledge reduction method in concept lattices based on 0–1 linear integer programming is proposed in this paper. Then, characterization theorems of three types attributes are obtained in attribute reduction, and attribute reduction method in concept lattices based on 0–1 linear integer programming is proposed.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (GrantNos. 11401469, 11701446) and the Natural Science Foundation of Shanxi Province (2018JM1055) and Natural Science Foundation of Fujian Province (2016J01310).

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Authors and Affiliations

  1. School of Science, Xian University of Posts and Telecommunications, Xian, 710121, China

    Lifeng Li

  2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xian University of Posts and Telecommunications, Xian, 710121, China

    Lifeng Li

  3. School of Science, Jimei University, Xiamen, 361021, China

    Dongxiao Zhang

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  1. Lifeng Li
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  2. Dongxiao Zhang
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Correspondence to Lifeng Li.

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Li, L., Zhang, D. 0–1 linear integer programming method for granule knowledge reduction and attribute reduction in concept lattices. Soft Comput 23, 383–391 (2019). https://doi.org/10.1007/s00500-018-3352-1

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