| As information technology and the Internet in-depth development, the Internet increasingly becoming the way people work, study and part of life. In the network use, Because of different habits and interests, users of the internet differ from one another in terms of information focus, time consumed and services selected. Thus different user groups will inevitably take on behavioral characteristics of their own. Knowing about the so-called group behaviors could gain, in a macro sense, an informed knowledge of the state of network use. On the one hand, this can provide some guidance for internet users, enabling them to make better use of network resources; on the other hand, this can also help network managers'anomaly detection, network traffic management. Campus network users, considered as one part of the internet users, the professionalism of its users, diversity and strong purpose use of the network determines the complexity of its flow than the average Internet user has more obvious features. Thus they may bear more obvious characteristics. This thesis focuses on this problem to find out the group behaviors to supply important theoretic foundation for network management, such as anomaly detection. As a result, the analysis of group behaviors on campus network is even more important.In this paper, the campus network users point of view, the data mining techniques applied to the user network behavior analysis which, according to Henan Polytechnic University campus network users access the actual situation, using the data mining Partition-based k-means clustering algorithm, This clustering algorithm will be optimized and applied to the user's most frequently used application protocol properties, improve the quality and accuracy of clustering. When analyzing the results, combined with the practical, Henan Polytechnic University campus network for users to access data from the corresponding statistical analysis done to make the results more comprehensive and more convincing. |
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