Decomposition of contexts into independent subcontexts based on thresholds

Abstract The process of decomposing databases into smaller datasets, with the objective of extrapolating the information obtained in the smaller ones to the original database, represents a relevant and complex challenge in real applications. It is particularly relevant in the context of fuzzy formal concept analysis, where the complexities of knowledge extraction from datasets characterized by incomplete and imperfect data are considerable. This paper will analyze a mechanism and different properties for detecting independent subcontexts from a given context, using modal operators within the multi-adjoint concept lattice framework.

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Publication Details

Journal
Computational and Applied Mathematics
Published
2025-06-30
DOI
https://doi.org/10.1007/s40314-025-03302-y
Primary Topic
Rough Sets and Fuzzy Logic
Type
article
Field-Weighted Citation Impact
0.00

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article

Decomposition of contexts into independent subcontexts based on thresholds

Jesús Medina, Eloísa Ramírez‐Poussa, Roberto G. Aragón
Computational and Applied Mathematics
Rough Sets and Fuzzy Logic
article

Decomposition of contexts into independent subcontexts based on thresholds

Jesús Medina, Eloísa Ramírez‐Poussa, Roberto G. Aragón
article en

Abstract

Abstract The process of decomposing databases into smaller datasets, with the objective of extrapolating the information obtained in the smaller ones to the original database, represents a relevant and complex challenge in real applications. It is particularly relevant in the context of fuzzy formal concept analysis, where the complexities of knowledge extraction from datasets characterized by incomplete and imperfect data are considerable. This paper will analyze a mechanism and different properties for detecting independent subcontexts from a given context, using modal operators within the multi-adjoint concept lattice framework.

Computational and Applied MathematicsVol. 44(7)
Universidad de Cádiz (ES)
European Commission, Universidad de Cádiz, Agencia Estatal de Investigación
Openalex Percentile: Top 83%
Rough Sets and Fuzzy Logic
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