Abstract: The rough set approach is an important tool to deal with uncertain or vague knowledge. Traditional rough set model on research of the minimal rule sets is based on static data. It cannot handle incremental data. However, the data in database is always incremental. Therefore incremental reduction of rules is a topic of general interest in the field of knowledge discovery. In this paper, an incremental learning method based on rough set theory and decision trees techniques is proposed. Then it is compared with classical and RRIA algorithm. The results show the method and effect of the algorithm are better.