Application of an Improved Sequence Pattern Association Rule Algorithm-based Data Management System for Continuing Education Teaching Data in Universities

Abstract A study proposes a university continuing education teaching data management system using an improved sequential pattern association rule algorithm. By introducing utility and interestingness parameters alongside support and confidence, the algorithm identifies efficient, engaging items. Experiments show it reduces computing time and eliminates up to 45% of known association rules, enhancing timeliness, accuracy, and speed in college education data mining management.