Knowledge maps are teaching tools that can promote deeply learning and avoid knowledge loss by helping students plan learning paths. Mining potential association rules of concepts from student exercise data was a common method to construct knowledge maps automatically. While manual conditions should be set to filter the association rules future to improve the accuracy of knowledge maps, which made the construction of the knowledge map can not automatic totally. So, the study proposed a knowledge map construction method that combined knowledge tracking and association rule mining expanding with interaction frequencies based on exercise data to achieve rules cleaning automatically. The method first predicted students' knowledge mastery sequences by a deep knowledge tracking model and discovered clustering relations to represent potential structures between concepts by fuzzy cluster analysis. Meanwhile, the method investigated association rule mining expanding with interaction frequencies to discover association rules between concepts. Finally, the clustering relations were used to filter the mined association rules automatically. To verify the effectiveness of the method, we constructed a knowledge map based on 34,350 online exercise data of 117 students in a computer programming course. Experimental results proved that the map was valid. Our implementations are available at https://github.com/xxdmw/FPGF-master.