摘要
大学生困难等级认定是教育扶贫中的一个重要环节,为避免认定过程过于主观而缺乏科学性,将模糊C均值聚类算法应用于学生信息数据的分析.首先对实验数据规格化,结合5个评定指标,建立模糊相似矩阵;然后采用模糊聚类分析的方法计算其传递闭包,得到模糊等价矩阵;最后按照等级认定要求加经验法检查分类结果的有效性.结果表明:使用模糊C均值聚类在完成大学生困难等级认定是可行的,能为实现教育精准扶贫提供参考依据.
The identification of college students’poverty level is an important part of education poverty alleviation.In order to avoid the subjective and unscientific identification process,the fuzzy C-means clustering algorithm is applied to the analysis of student information data.Firstly,the experimental data are normalized.Secondly,the fuzzy similarity matrix is established by combining five evaluation indexes.Then the fuzzy cluster analysis method is used to calculate the transitive closure,and the fuzzy equivalent matrix is obtained.Finally,the validity of the classification results is checked by the empirical method according to the grade identification requirements.The results show that the use of fuzzy C-means clustering in the completion of college students’poverty level identification of ten stars can provide a reference for the realization of targeted poverty alleviation in education.
作者
刘晓娜
王恺
徐彦强
LIU Xiao-na;WANG Kai;XU Yan-qiang(School of Digital Media,Lanzhou University of Arts and Science,Lanzhou 730000,China;Audit Office,Lanzhou University of Arts and Science,Lanzhou 730000,China;Department of Computer Science,Lanzhou Institute of Technology,Lanzhou 730050,China)
出处
《兰州文理学院学报(自然科学版)》
2023年第4期64-67,共4页
Journal of Lanzhou University of Arts and Science(Natural Sciences)
基金
甘肃省高等学校创新基金项目(2020B-256)。
关键词
困难认定
模糊聚类
隶属度
聚类分析
FCM算法
聚类有效性
poverty identification
fuzzy clustering
subordinate degree
cluster analysis
FCM algorithm
clustering validity