The article presents a new method for creating decision trees that help classify data effectively. By combining rough set theory and distance functions, the researchers developed an algorithm that selects attributes based on their importance and reduces the height of the tree. This approach improves the readability of the classification rules and enhances the overall performance of decision trees in data mining.