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

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.