Construction of Rough graph to handle uncertain pattern from an Information System

TL;DR

This paper introduces a novel method for constructing rough graphs using rough membership functions to better handle uncertain and imprecise patterns in information systems.

Contribution

It proposes a new approach to build rough graphs based on rough membership functions, enhancing pattern recognition in uncertain data environments.

Findings

Rough graphs effectively model uncertain relationships.

The method improves pattern detection in imprecise data.

Properties and operations of rough graphs are systematically explored.

Abstract

Rough membership function defines the measurement of relationship between conditional and decision attribute from an Information system. In this paper we propose a new method to construct rough graph through rough membership function $\omega_{G}^F(f)$. Rough graph identifies the pattern between the objects with imprecise and uncertain information. We explore the operations and properties of rough graph in various stages of its structure.