Abstract
The access logs of the flow control server in the campus network of A university are extracted and analyzed in this paper. A hybrid clustering combined with sampling, K-means algorithm and agglomerative hierarchical method is proposed to analyze users’ behavior and classify users’ access objectives and habits, which can not only make clustering results more stable, but also enhance the analysis efficiency of the algorithm.
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Jiang, H., Yu, Q., Xu, Y. (2020). Clustering Optimization and Evaluation of Campus Network User Behavior Analysis System. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_11
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DOI: https://doi.org/10.1007/978-3-030-32591-6_11
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