Application of Improved Association Rule Algorithm in Higher Education Teaching Management

Data mining is an innovative approach to data analysis that sifts through vast quantities of data stored in the digital realm in search of useful information. One data mining methodology called association rule mining uses computational methods to discover numerous related rules in massive datasets. This paper presents the creation of an information-based teaching management system that incorporates association rule mining techniques to improve the level and efficiency of university teaching management. Each system relies on its client, server, and database. The server runs the system program logically, while the client handles user interaction. In order to get educational data pertaining to users, the system builds a database that includes all the necessary information about teaching management and uses better association rule mining algorithms. Teaching, course selection, and course services are all made available to users through the client interface. The performance of the system is tested using a university information platform. The results show that the system can achieve course query functionality and has good client compatibility. It can be applied in various browsers and operating systems, demonstrating strong applicability and excellent data mining performance.