An algorithm called SDMAR was created to reduce the number of attributes in a dataset by using a simplified discernibility matrix. This helps save memory and reduce the time needed to process the data. By merging similar attributes and removing redundant information, the algorithm efficiently identifies and eliminates the most common attributes until the discernibility matrix is empty. The approach was shown to significantly lower the time complexity of attribute reduction, making data analysis faster and more manageable.