An Algorithm for Data Management of Higher Education Based on Fuzzy Set Theory - Association Rule Mining Algorithm

Abstract

Data management can enhance the efficiency of management in higher education. When combined with data mining and other technologies, it can offer a sound basis for making management decisions. This article combined the Apriori algorithm in association rules with fuzzy theory and optimized the FCM algorithm to mine fuzzy association rules for student course grades. The results indicated that the improved FCM algorithm demonstrated a more effective clustering effect on Iris, and the outcomes were closer to the actual values. Applying this method to fuzzy association rule mining could reveal the connections between students' course grades. For instance, when students achieved excellent grades in the course of computer application fundamentals, their performance in principles of computer composition was also good, and if they obtained excellent grades in computer application fundamentals and good grades in principles of computer composition, their grades achieved in operating systems were excellent as well. The experimental results validate the reliability of the fuzzy association rule mining algorithm, which enables the discovery of associations between different courses. Consequently, it provides valuable support for education and teaching.