Dynamic early warning system of College Students’ target course performance based on improved Apriori algorithm

Abstract

The early warning system of College Students’ target course achievement is an important part of the educational administration system in Colleges and universities. This paper proposes to use some techniques of association principle to mine a large amount of data in the performance system to a certain extent, and obtain available rules from the data. Based on the characteristics and shortcomings of Apriori algorithm, an improved Apriori is proposed. The algorithm can process and mine the data in the early warning system of College Students’ scores, and finally obtain the management principles, thus forming an effective early warning for the course learning. In order to promote the improvement of students’ academic performance and achieve the ultimate goal of cultivating excellent talents in Colleges and universities.

The Application of Multi-Source Big Data Mining Techniques in the Analysis of Basketball Economic Management

“…How to establish the association relationship between each influencing factor and extract the important factors influencing basketball economic management from many factors is the key to develop measures to improve the basketball economic industrialization. To this end, this part of the research uses the unsupervised learning algorithm (Apriori) [16][17][18] in machine learning to establish association rules between each influencing factor and obtain the key factors affecting the basketball economic management by analyzing the strong association rules, which provides a basis for the subsequent formulation of measures to improve the industrialization of the basketball economy.…”