基于k均值与SVM算法的学生综合评价及分类研究

Research on Comprehensive Evaluation and Classification of Students Based on K-means Clustering and SVM Algorithms

随着高校招生规模扩大,对学生的考试成绩进行科学分析以便于专业分流和分级教学是目前高校管理工作中面临的一个重要问题。本文提出了一种基于k均值与SVM算法的半监督学习模型,首先利用因子分析提取学生的综合能力指标,然后通过k均值聚类对部分数据进行初步分类标注,最后利用训练得到的支持向量机模型对其他数据进行分类。模型验证结果表明本文构建的数学模型可以准确地区分不同特质的学生,高效地对大量学生做出合理的分类,对于高校管理和教学改革具有明确的指导意义。

With the expansion of college enrollment,scientific analysis of students'test scores is an important issue in the management of colleges and universities in order to facilitate professional diversification and hierarchical teaching.In this paper,a semi-supervised learning model based on k-means and SVM algorithms is proposed.Firstly,factor analysis is used to extract the comprehensive ability components of students,then k-means clustering is used to classify and label some data,and finally,the support vector machine model obtained by training is used to classify other data.The experiment shows that the model constructed in this paper can accurately distinguish students with different characteristics in high efficiency,which can guide the management and teaching reform of colleges and universities significantly.