基于K-means算法的研究生入学成绩分析

Analysis of Enrollment of Graduate Students Based on K-means Algorithm

研究生入学成绩是导师初步了解学生学习能力、学习风格、制定研究生培养方案的重要参考指标。随着学校招生规模的扩大,学生人数的增加,研究生入学成绩的日趋复杂,传统的分析方法已经不能满足当前对于研究生入学成绩分析的需要。通过应用K-means聚类算法对研究生入学成绩进行分析,将研究生入学成绩进行分类,发现学生成绩分布的特点,找出成绩之间的关系,了解学生各科的学习状况,找到适合学生发展的方向,以实现个性化的研究生教育和培养,所得结果为研究生培养方案的制定与研究生进行研究方向的选择提供了借鉴意义。首先,分析了几种主要聚类算法应用于研究生入学成绩的适用性;其次,介绍了K-means聚类算法;最后,对研究生入学成绩进行数据分析、预处理。通过实验证明了K-means聚类算法在研究生入学成绩分析中的实用性。

Postgraduate enrollment is an important reference for instructors to understand students’learning ability,learning style and development of postgraduate training programs.With the expansion of enrollment scale,the increase of students and the growing complexity of the postgraduate enrollment,the traditional analysis methods can no longer meet the current needs of graduate enrollment analysis.Through the application of K-means clustering algorithm to analyze the results of graduate enrollment,the graduate enrollment score is classified to find out the characteristics of student achievement distribution and the relationship between achievements,and the learning situation of students in each subject is understood to find a direction suitable for student development,achieving personalized graduate education and training.The results provide a reference for the formulation of postgraduate training programs and the selection of research direction for graduate students.Firstly,the applicability of several major clustering algorithms for graduate student enrollment performance is analyzed.Secondly,the K-means clustering algorithm is introduced.Finally,the data of graduate enrollment grades are analyzed and preprocessed.The practicability of K-means clustering algorithm in the analysis of postgraduate enrollment score is demonstrated by experiments.