基于数据挖掘和K-Means算法的高校学情数据集成研究  

Research on the integration of university academic information data based on data mining and K-means algorithm

作　　者：李凤英 许洪光 周方 李培[2] LI Fengying;XU Hongguang;ZHOU Fang;LI Pei(School of Artificial Intelligence,Hebei Oriental University,Langfang 065000,China;Institute of Geophysical and Geochemical Exploration,Chinese Academy of Geological Sciences,Langfang 065000,China)

机构地区：[1]河北东方学院人工智能学院,河北廊坊065000 [2]中国地质科学院地球物理地球化学勘查研究所,河北廊坊065000

出　　处：《黑龙江工程学院学报》2022年第4期31-36,共6页Journal of Heilongjiang Institute of Technology

基　　金：河北东方学院校级重点课题(HDYXJZD1910)。

摘　　要：为了更好地了解学生,为教学设计提供依据,基于数据挖掘和K-Means算法对高校学情数据集成进行研究。首先对学生数据进行预处理,利用Python技术可视化分析学生成绩与性别的深层关系,构建RET标签体系;然后利用K-Means对学生数据进行聚类分析。实验结果表明,该方法的聚类分析结果边界清晰、效果较好,教师可以据此深入了解学生情况。In order to better understand the students and provide the basis for the teaching design,the university learning situation data integration research based on data mining and K-Means algorithm is proposed.The student data is Preprocessed;Python technology is used to visually analyze the deep relationship between student achievement and gender,constructing the RET labeling system;Student data is analyzed by K-Means.The experimental result shows that the clustering analysis results of this method have clear boundaries and good results,from which teachers can preferably understand the student situation.

关 键 词：数据挖掘 学情分析 K-MEANS算法 PYTHON 可视化分析 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]