College Student Management System Based on K-means Clustering Algorithm

Abstract

Student management is a problem that every university needs to solve. The purpose of this paper is to design a college student management system based on K-mean clustering algorithm. The purpose of this campus project analysis and research is to help teachers fully understand, master and adapt to students' skills, achieve the purpose of teaching, and write and develop optimized K-means algorithms. Firstly, through the analysis of the student management data, the system function module is added to the student management system. Then the number of K clusters is selected to set the total range, and the optimal value of the number of K clusters is selected by calculating the ratio of the inner and outer distances. Use the K-means method to analyse the optimization of the algorithm to analyse the performance on student management. The optimized K-mean algorithm has completed the classification of students well, and the average score of Pmoral Learning is 17.54 for the first category. College student management is a very important innovation, which has played a certain role in promoting the development of student management.