基于模糊K均值聚类的高校网络用户行为分析  

Analysis of university network users behavior based on fuzzy K-means clustering

作　　者：于莉佳 汪涛[2] YU Lijia;WANG Tao(Harbin Software Research Institute of China Unicom,Harbin 150040,China;Harbin University of Commerce,Harbin 150028,China)

机构地区：[1]中国联合网络通信有限公司哈尔滨软件研究院,哈尔滨150040 [2]哈尔滨商业大学,哈尔滨150028

出　　处：《智能计算机与应用》2022年第10期200-202,共3页Intelligent Computer and Applications

摘　　要：随着网络科技的迅猛发展,互联网用户的规模正在以指数的速度不断增长。高校网络用户的规模也随着互联网兴起而出现大规模增长。对高校网络用户的上网行为进行分析,能够更好地掌握在校学生的动态,为学校制定科学、高效的互联网管理方式奠定了更加客观的数据基础。本文首先将高校网络用户上网行为进行分类,然后通过模糊K均值聚类算法对学生的上网行为进行分类。实践表明,通过对某高校的学生上网行为展开分析,为该校的互联网管理和学生的精细化管理提供了有利的数据支撑。With the rapid development of network technology, the scale of Internet users is growing exponentially. Consequently, the scale of network users in colleges and universities has also grown massively with the rise of the Internet. By analyzing the online behavior of network users in colleges and universities, the dynamics of students in schools coud be better grasped, and more objective data foundation could be established for schools to formulate scientific and efficient Internet management methods. This paper first classifies the online behaviors of college network users, and then uses the fuzzy K-means clustering algorithm to classify the online behaviors of students. Practice shows that analyzing the online behavior of students in an university could provide favorable data support for the school’s Internet management and refined management of students.

关 键 词：模糊K均值聚类算法 网络用户行为 数据挖掘 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]