Research on Cluster Analysis of College Grades Based on Optimized K-means Algorithm
In response to the problem of unstability in clustering results that is caused by sus-ceptibility of the classical K-means algorithm in the clustering center to outliers,this paper pro-poses an optimized K-means algorithm based on sample distribution density to improve the stabili-ty and accuracy of clustering.After clustering,the methods of CH index and overall percentage of classification intervals are used to objectively evaluate the three discretization methods.The re-sults show that the optimized K-means algorithm can avoid irrationality of interval classification and reflect distribution characteristics of grade samples more accurately.
mean algorithmdistribution densityclusteringK-means
NETL
NSTL
万方数据
维普
