The application of association rules and interestingness in course selection system
Zhimin Chen, Wei Song, Lizhen Liu · 2017
With the development of social economy and the improvement of cultural and educational level, the enrollment of all the major colleges and universities in our country has been expanding constantly. The arrangement of courses of the school has become more and more complicated. It has become a common phenomenon that students of colleges and universities select courses by online course selection system. In this paper, we combine the association rule of the data mining technology with interestingness measure threshold, and apply them to course selection system. Firstly, we set the support threshold, the confidence threshold and the interestingness measure threshold. Then we mine the association rules in the course information database. Finally, according to the relevance of the courses and association rules, we filter out the better association rules in the resulting association rules. The proposed method can provide students with better course selection programs without teachers' arrangements, and it saves time and money for school.