基于改进K-Means的大学生体质健康评价细分模型研究

Study on a Model of Physical Health Evaluation of College Students Based on Improved K-Means

为提高大学生体质健康数据利用率,通过聚类分析对数据进行分组,为大学生体质分层次教学提供决策支持。在K均值聚类算法基础上,分别从K值选取与初始点选取两方面进行算法改进。实验结果表明,改进的K均值聚类算法效率更高,分组结果更加合理且易于解释,可为学校制订智能化运动处方提供有效参考。

In order to improve the physical health data utilization rate of college students, the data were grouped by cluster analy sis, and the decision support was provided for college studentsr physique stratification teaching. On the basis of k means cluste ring algorithm, the algorithm is improved based on the selection of K value and the selection of initial point. The experimental results show that the improved k-means clustering algorithm is more efficient, grouping results are more reasonable and easier to explain, it provides effective reference for schools to carry out the intelligent exercise prescription.