The researchers developed a new algorithm based on rough sets theory to improve the efficiency of attribute reduction in decision tables. By changing the constraint condition in searching for core attributes, the algorithm can efficiently find a good subset of attributes with low time complexity, especially for large data sets. Experiment results demonstrate that the algorithm is effective in reducing attributes and finding a useful subset.