Rough set analysis of graphs
Filomat, Tome 36 (2022) no. 10, p. 3331
Voir la notice de l'article provenant de la source eLibrary of Mathematical Institute of the Serbian Academy of Sciences and Arts
Relational data has become increasingly important in decision analysis in recent years, and so mining knowledge which preserves relationships between objects is an important topic. Graphs can represent the knowledge which contains objects and relationships between objects. Rough set theory provides an effective tool for extracting knowledge, but it is not sufficient to extract the knowledge containing the data on relationships between objects. In order to extend the application scope and enrich the rough set theory, it is essential to develop a rough set analysis of graphs. This extension is important because graphs play a crucial role in social network analysis. In this paper, the rough set analysis of graphs based on general binary relations is investigated. We introduce three types of approximation operators of graphs: vertex graph approximation operators, edge graph approximation operators, and graph approximation operators. Relationships between approximation operators of graphs and approximation operators of sets are presented. Then we investigate the approximation operators of graphs within constructive and axiomatic approaches.
Classification :
68T30, 68T37
Keywords: Rough sets, generalized approximation space, lower and upper approximations, axioms
Keywords: Rough sets, generalized approximation space, lower and upper approximations, axioms
Sha Qiao; Ping Zhu; Witold Pedrycz. Rough set analysis of graphs. Filomat, Tome 36 (2022) no. 10, p. 3331 . doi: 10.2298/FIL2210331Q
@article{10_2298_FIL2210331Q,
author = {Sha Qiao and Ping Zhu and Witold Pedrycz},
title = {Rough set analysis of graphs},
journal = {Filomat},
pages = {3331 },
year = {2022},
volume = {36},
number = {10},
doi = {10.2298/FIL2210331Q},
language = {en},
url = {http://geodesic.mathdoc.fr/articles/10.2298/FIL2210331Q/}
}
Cité par Sources :