Analysis of Student Size at Different Stages in Guizhou Province Based on Improved K-Means Clustering Algorithm

Abstract

 In view of the shortcomings of the original K-means algorithm that it is sensitive to distant group points and the K value is difficult to determine, an improved K-means clustering algorithm is designed on this basis, and the idea of elbow method is introduced to optimize the data to determine the number of clustering clusters K and the clustering center of clusters, and the cluster center is calculated on the basis of the K-means algorithm, and the improved K-means algorithm has a significantly better effect than the K-means algorithm. An improved k-means clustering algorithm was proposed to analyze the number of students at each stage in Guizhou Province. On this basis, the influencing factors are briefly analyzed and the following countermeasures are put forward to scientifically grasp the trend of population change and optimize the supply of various types of educational resources at all levels. Based on the urban development planning layout of prefecture-level cities, strengthen the overall design of the education of the children of the floating population; Improve mechanisms for sharing high-quality educational resources, and promote the high-quality and balanced development of compulsory education.