Analysis and Research of the Campus Network User's Behavior Based on k-Means Clustering Algorithm

This thesis introduces the status and methods of data mining, aiming at the Nantong University campus network users access data preprocessing analysis, using the K-means clustering algorithm combined with SQL Server 2008 and Visual Studio 2008 business intelligence project function for data mining analysis, and the mining experimental results are analyzed and studied. The research indicates that the campus network users of Internet time has a positiver relevance with the rate of student's failing grades and a negative correlation with getting schlolarship and CET4(College English Test 4) achievements. What's more, it not only has a positive effect on school leaders fully understand the behavioral characteristics of students and campus network users of campus network usage, timely feedback and guiding the students to form a good habit of learning, but also plays an important role in improving the campus network bandwidth, performance and application efficiency.