Reducts of fuzzy contexts: Formal concept analysis vs. rough set theory
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909995039195136 |
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| author | Chen, Yuxu Liu, Jing Shen, Lili Tang, Xiaoye |
| author_facet | Chen, Yuxu Liu, Jing Shen, Lili Tang, Xiaoye |
| contents | We postulate the intuitive idea of reducts of fuzzy contexts based on formal concept analysis and rough set theory. For a complete residuated lattice $L$, it is shown that reducts of $L$-contexts in formal concept analysis are interdefinable with reducts of $L$-contexts in rough set theory via negation if, and only if, $L$ satisfies the law of double negation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13059 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reducts of fuzzy contexts: Formal concept analysis vs. rough set theory Chen, Yuxu Liu, Jing Shen, Lili Tang, Xiaoye Logic in Computer Science 68P05, 03G10, 03B52 We postulate the intuitive idea of reducts of fuzzy contexts based on formal concept analysis and rough set theory. For a complete residuated lattice $L$, it is shown that reducts of $L$-contexts in formal concept analysis are interdefinable with reducts of $L$-contexts in rough set theory via negation if, and only if, $L$ satisfies the law of double negation. |
| title | Reducts of fuzzy contexts: Formal concept analysis vs. rough set theory |
| topic | Logic in Computer Science 68P05, 03G10, 03B52 |
| url | https://arxiv.org/abs/2509.13059 |