Method for Restoring Consistency in Inconsistent Data Sets

Real-world data are often inconsistent due to various factors. Rough set theory provides tools for analyzing such inconsistencies but does not aim to eliminate or correct them. Instead, it represents uncertainty through the use of approximations. In this paper, a method for restoring data consistency is proposed by identifying the concept to which the cases are homogeneous with the inconsistent case. The experimental results show that the rules induced from consistent sets and generated using the DCR method achieve comparable effectiveness, and the difference between these approaches is not significant in terms of rule complexity.