基于聚类和顺序聚类的高校数据挖掘分析  

Analysis of University Data Mining Based on Clustering and Sequential Clustering

作　　者：高建平 董东[1] GAO Jian-ping;DONG Dong(College of Computer and Cyber Security,Hebei Normal University,Shijiazhuang 050024,China)

机构地区：[1]河北师范大学计算机与网络空间安全学院,河北石家庄050024

出　　处：《电脑知识与技术》2020年第25期52-54,共3页Computer Knowledge and Technology

基　　金：河北省教育厅省级专业学位教学案例(库)(KCJSZ2020036)项目支持

摘　　要：针对高校一卡通系统中大量消费数据和图书馆系统的访问数据,设计并实现了学生日常行为聚类模型,根据行为习惯将学生划分为五大类,利用Microsoft顺序聚类算法实现了学生行为序列的挖掘,发现了“体弱”人群存在不规律饮食习惯等有意义的行为序列模式,最后针对体弱人群在两个模式上的共性和差异进行总结。In order to find interesting patterns from a large amount of consumption data accumulated in campus card systems and history data from library access control systems,a daily behavior clustering model for college students was designed and implement⁃ed.It is found that students can be divided into five categories based on behavioral habits.Moreover,by the Microsoft sequential clustering algorithm for mining of student behavior sequences,several meaningful patterns of behavioral sequences,such as"weak"people have irregular eating habits,is discovered,and finally the commonalities and differences between the two groups of weak people are compared.

关 键 词：一卡通 智慧校园 校园数据 数据挖掘 行为分析 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]