Clustering Optimization and Evaluation of Campus Network User Behavior Analysis System

  • Hong Jiang
  • Qingsong YuEmail author
  • Yingying Xu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)


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.


User behavior analysis Campus network Data mining Cluster analysis 


  1. 1.
    Anderson, E.L., Steen, E., Stavropoulos, V.: Internet use and problematic Internet use: a systematic review of longitudinal research trends in adolescence and emergent adulthood. Int. J. Adolesc. Youth 22(4), 430–454 (2017)CrossRefGoogle Scholar
  2. 2.
    Kimberly, Y.: The evolution of Internet addiction disorder, Internet addiction. In: Studies in Neuroscience, Psychology and Behavioral Economics, pp. 3–18. Springer, Cham (2017)Google Scholar
  3. 3.
    Mohsen, S., Mohammad, I., Suhaiza, Z., Goh, G.: An empirical investigation of campus portal usage. Educ. Inf. Technol. 23(2), 777–795 (2018)CrossRefGoogle Scholar
  4. 4.
    Yoshioka, R.I., Lucila, I.: An adaptive test analysis based on students’ motivation. Inform. Educ. 17(2), 381–404 (2018)CrossRefGoogle Scholar
  5. 5.
    Kate, T.: Social (network) psychology: how networks shape performance, persistence, and access to information. ProQuest Dissertations Publishing, Columbia University (2019)Google Scholar
  6. 6.
    Xuxiao, G., Qun, S., Zhongnan, F., Jie, Q.: Multi-dimensional behavior analysis and optimization of traffic in campus network. J. Huazhong Univ. Sci. Technol. (Nat. Sci. Ed.) 44(z1), 131–137 (2016)Google Scholar
  7. 7.
    Ling, W., Jiagui, Y., Jinsong, C., Pingshui, W.: Research and implementation of VPN access log analysis platform based on Hadoop. J. Shenyang Univ. (Nat. Sci.) 28(6), 488–496 (2016)Google Scholar
  8. 8.
    Zhengguo, Z.: Analysis and research of campus network users’ behavior based on K-means algorithm. J. Anqing Teachers Coll. (Nat. Sci. Ed.) 23(1), 53–56 (2017)Google Scholar
  9. 9.
    Chou, C.-H., Hsieh, S.-C., Qiu, C.-J.: Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction. Appl. Soft Comput. 56, 298–316 (2017)CrossRefGoogle Scholar
  10. 10.
    Wang, L.P.: On competitive learning. IEEE Trans. Neural Netw. 8(5), 1214–1217 (1997)CrossRefGoogle Scholar
  11. 11.
    Arora, P., Varshney, S.: Analysis of k-means and k-medoids algorithm for big data. Procedia Comput. Sci. 78, 507–512 (2016)CrossRefGoogle Scholar
  12. 12.
    Mazzeo, G.M., Carlo, M.Z.: A fast and accurate algorithm for unsupervised clustering around centroids. Inf. Sci. 400–401, 63–90 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer CenterEast China Normal UniversityShanghaiChina

Personalised recommendations