Publication

Abstract : In this study, we propose to calculate multiple metric dimensions using different distances. This approach can lead to the implementation of dimensionality reduction techniques for a specific information system. By combining traditional graph theory with rough set theory, which involves using uncertain or ambiguous data, we can construct a rough graph to depict the relationships between attributes. The rough graph is constructed based on the rough membership function, which defines the link between the conditional and decision features. By utilizing degree-based metric dimensions, we can identify and remove inconsistent features from the information system. If each vertex's vector of distances from the other vertices in the set is unique, it means that the set of vertices can fully determine the graph. The metric dimension, which represents the smallest cardinality of a resolving set, plays a role in facilitating navigation and aiding in location determination within the graph.