The article introduces a new method for reducing the number of attributes in data analysis, which is important in rough set theory. The researchers propose a heuristic algorithm based on discernibility sets to find key attributes efficiently. By defining discernibility sets and using two methods to identify key attributes, they show that the algorithm is effective in reducing data complexity. The algorithm is proven to be correct and complete, and it helps in minimizing the number of attributes needed for analysis. Both theoretical analysis and experiments confirm the algorithm's effectiveness and efficiency in handling large datasets.