A new algorithm has been developed to simplify complex decision-making processes in Rough Set Theory. By focusing on finding the best decision rules using the shortest combinations of attributes, the algorithm ensures that all samples are accurately represented. To make the process more efficient, the algorithm introduces concepts like bound coefficient, classifying samples with the same decision values together, and defining the Remain set. These methods not only reduce computational complexity but also make the algorithm applicable to incomplete information systems.