Digitalization of university education management applicable to association rule mining algorithms

Abstract

In this paper, firstly, based on the digital construction of education management in colleges and universities, it is proposed to construct an education management system for colleges and universities to realize the digital development of education management and to design the function and database of the education management system of colleges and universities. Secondly, the Apriori algorithm in association rule mining algorithm is used to define the minimum support and minimum confidence in the variable parameter focus, then the support threshold and confidence threshold are set in the process of optimization connection and pruning, and for the shortcomings of traditional algorithms, the relevance metrics are cited here to improve the systematic data mining efficiency of the Apriori algorithm. Then, the simulation experiment environment and data set are selected, the evaluation index system is determined according to the system requirements, and the education management system based on the association rule mining algorithm is simulated and analyzed. The results show that when min_supp=70%, the Apriori algorithm generates a total of 60 frequent itemsets, and the running time is about 21.2s. Overall, the Apriori algorithm only reads the data set twice, so the program running time is shorter than the Eclat algorithm running time.