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Common-possible concept analysis: A granule description viewpoint

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Abstract

Concepts are basic units of human cognitive activities and concept based granule description is one of the possible ways to realize explainable AI through information granules. However, the existing types of concepts cannot work effectively when it is necessary to simultaneously investigate the common attributes and possible attributes of granules. In order to solve this problem, we propose common-possible concept analysis based on a granule description viewpoint. Concretely, common-possible concepts are proposed, which can concurrently describe the common attributes and possible attributes of granules. Then, a method to acquire common-possible concepts from formal contexts is described. Besides, the connections between common-possible concepts and the other types of concepts are explored. Analysis results show that common-possible concept analysis can effectively deal with a type of problems and provides some details of granules different from that of formal concepts and property oriented concepts.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61772021, 11801440 and 61976244), Natural Science Basic Research Program of Shaanxi (Program No. 2021JM-141) and the Fundamental Research Funds for the Universities of Henan Province (Program No. NSFRF210318).

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Authors and Affiliations

  1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, Henan, People’s Republic of China

    Huilai Zhi

  2. School of Computer Science and Technology, Xidian University, Xi’an, 710071, Shaanxi, People’s Republic of China

    Jianjun Qi

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  1. Huilai Zhi
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Zhi, H., Qi, J. Common-possible concept analysis: A granule description viewpoint. Appl Intell 52, 2975–2986 (2022). https://doi.org/10.1007/s10489-021-02499-9

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