基于异常点检测的大学生异质行为分析  

Heterogeneous Behavior Analysis of College Students Based on Outlier Detection

作　　者：彭琳[1] 宋珺 刘安栋 熊玲珠[1] PENG Lin;SONG Jun;LIU Andong;XIONG Lingzhu(School of Computer Science,Jiangxi University of Chinese Medicine,Nanchang 330004,China)

机构地区：[1]江西中医药大学计算机学院,江西南昌330004

出　　处：《软件导刊》2024年第4期193-198,共6页Software Guide

基　　金：江西省高校人文社会科学研究项目(JC19125)。

摘　　要：大学生异质行为指的是大学生具有个性特征、不同于他人的行为偏好。针对大学生异质个体的行为挖掘问题,提出一种基于异常点检测的异质行为分析方法。首先以某校大学生成绩数据和校园一卡通数据为基础,建立异质行为分析模型,采用主成分分析、K-Means++和DBSCAN聚类分析寻找异常点,研究关注异常点对应的异质行为人。然后,通过异常点检测辨别学习成绩中的异质个体,并进一步探究其作息规律与学习成绩异常之间是否存在强关联。接下来,运用多种算法相互印证异常点的准确性,借助对相关学生的调研来验证异常点数据的可信度。研究表明,所提方法能对大学生异质行为模式进行深度分析,为提升学校管理水平和管理效率提供了基础依据。Heterogeneous behavior of college students refers to the behavioral preferences of college students with individual characteristics that are different from others.Aiming at the behavior mining problem of heterogeneous individuals of college students,a heterogeneous behav-ior analysis method based on anomaly detection is proposed.A heterogeneous behavior analysis model is established based on the college stu-dent's performance data and campus one-card data of a university.Principal component analysis,K-Means++,and DBSCAN clustering anal-ysis are used to find the weird points,and the research focuses on the heterogeneous behaviors corresponding to these anomalous points.Even-tually,through detecting anomalies,heterogeneous individuals in academic performance can be identified and further explored whether there is a strong correlation between work and rest patterns and academic performance anomalies.The authenticity of these anomalies is verified from both algorithmic and factual dimensions,firstly,multiple algorithms are used to verify the accuracy of the anomalies;secondly,the credibility of the anomaly data is verified with the help of research on related students.Through this study,the heterogeneous behavioral patterns of col-lege students can be analyzed in depth,providing a basic basis for improving schools'management levels and efficiency.

关 键 词：异质性 行为分析 聚类算法 主成分分析 异常点检测 

分 类 号：G434[文化科学—教育学]