Impact of local congruences in variable selection from datasets

Formal concept analysis (FCA) is a useful mathematical tool for obtaining information from relational datasets. One of the most interesting research goals in FCA is the selection of the most representative variables of the dataset, which is called attribute reduction. Recently, the attribute reducti...

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Bibliographic Details
Published in:Journal of computational and applied mathematics 2022-04, Vol.404, p.113416, Article 113416
Main Authors: Aragón, Roberto G., Medina, Jesús, Ramírez-Poussa, Eloísa
Format: Article
Language:English
Subjects:
Congruence relation
Formal concept analysis
Size concept lattice reduction
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Summary:Formal concept analysis (FCA) is a useful mathematical tool for obtaining information from relational datasets. One of the most interesting research goals in FCA is the selection of the most representative variables of the dataset, which is called attribute reduction. Recently, the attribute reduction mechanism has been complemented with the use of local congruences in order to obtain robust clusters of concepts, which form convex sublattices of the original concept lattice. Since the application of such local congruences modifies the quotient set associated with the attribute reduction, it is fundamental to know how the original context (attributes, objects and relationship) has been modified in order to understand the impact of the application of the local congruence in the attribute reduction.
ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2021.113416