[Objective] This study reveals the private colleage students’ typical online life styles based on their usage of a navigational Web portal. [Methods] First, we collected the click and search data of the navigation page specifically designed for students. Then, we modeled the data and applied the K-means cluster algorithm to categorize the student behaviors. [Results] We found six major behaviors among private college students. However, these students mainly use the Web to watch videos, while only a small number of students use the Web to learn. [Limitations] The size and dimensions of the data need to be expanded. [Conclusions] This study identifies typical online life styles of private college students, which could help schools improve their administraion and services.
(Qi Liangyan, Xu Yueying.An Investigation and Analysis of Students’ Leisure Life in Private Colleges and Universities in Shanghai[J]. Journal of Zhejiang Shuren University: Humanities and Social Sciences, 2010(4): 124-128.)
(Lin Hong.A Comparative Study on the Internet Dependence of Private College Students[J]. Youth and Adolescence Studies, 2008(6): 24-28.)
doi: 10.3969/j.issn.1673-8950.2008.06.008
(Zhang Yufeng, He Chao.Research on Dynamic Competitive Intelligence Analysis Based on Web Log Mining[J]. Information Studies: Theory & Application, 2011, 34(9): 51-53.)
(Zhang Wenjun, Wang Jun, Xu Shanchuan.Clustering Analysis of Demand State of E-commerce Users - Taking Taobao Women’s Clothing as an Example[J]. New Technology of Library and Information Service, 2015 (3): 67-74.)
[10]
Prasad P, Malik L G.Generating Customer Profiles for Retail Stores Using Clustering Tech[J]. International Journal on Computer Science & Engineering, 2011, 3(6): 2506-2510.
[11]
Moe W W.Buying, Searching, or Browsing: Differentiating Between Online Shoppers Using In-Store Navigational Clickstream[J]. Journal of Consumer Psychology, 2003, 13(1-2): 29-39.
doi: 10.1207/S15327663JCP13-1&2_03
[12]
于亚秀. 基于Web日志挖掘的个性化服务研究[D]. 上海: 华东师范大学, 2009.
[12]
(Yu Yaxiu.Research on Personalized Service Based on Web Usage Mining [D]. Shanghai: East China Normal University, 2009.)
[13]
Jain A K.Data Clustering: 50 Years Beyond K-means[J]. Pattern Recognition Letters, 2010, 31(8): 651-666.
doi: 10.1016/j.patrec.2009.09.011
