Analysis of Postgraduate Admission Scores Based on K-means Algorithm

Postgraduate admission score are important reference indicator for the supervisor to understand the learning ability and learning style of students, and formulate postgraduate education programs. With the expansion of the university enrollment scale, the increase in the number of students and in the complexity of postgraduate admission scores, the traditional analysis method cannot meet the current needs for the analysis of postgraduate admission scores. In this paper, the K-means clustering algorithm was used to analyze and classify the postgraduate admission scores, so as to discover the characteristics of student score distribution, find out the relationship among the scores, and find the direction suitable for students' development by understanding their learning status in various subjects, thereby achieving personalized postgraduate education and training. The result provides reference for the formulation of postgraduate education programs and the choice of research directions for postgraduates. First, the applicability of several major clustering algorithms applied to postgraduate admission scores was analyzed; second, the K-means clustering algorithm was introduced; finally, data analysis and preprocessing were performed on postgraduate admission scores, and through the experiment, the practicability of K-means clustering algorithm in the analysis of postgraduate entrance scores was illustrated.