Predicting the College Students Campus Network Behaviour Using Data Mining Techniques

The growth and promotion of big data methods will enable teachers to understand student behavior patterns in a timely and accurate manner, especially those that need to focus on student groups in a timely manner and help promote student affairs management. Empirical qualitative knowledge for scientific quantitative analysis. This paper covers the clustering method of data mining, to analyze the campus network behavior of 3,245 students at a particular grade B university, gaining a total of 23.843 million Internet access data over 4 years. The result shows groups of 4 students with different characteristics of Internet access, finding 350 students with large network usage. The successes and other aspects of these students 'performance are affected. This study conducted Data Mining of Student Campus Network Behavior, which serves as a practical operation case for student affairs management data mining, providing effective data support for the accurate and scientific development of student affairs management.