[1]李 博,李 霞,张 晓,等.MD-KNN 算法在高校精准资助中的应用[J].计算机技术与发展,2020,30(07):91-95.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 020]
　LI Bo,LI Xia,ZHANG Xiao,et al.Application of MD-KNN in Accurate Subsidy of Colleges[J].,2020,30(07):91-95.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 020]
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MD-KNN 算法在高校精准资助中的应用()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年07期
页码:
91-95
栏目:
应用开发研究
出版日期:
2020-07-10

文章信息/Info

Title:
Application of MD-KNN in Accurate Subsidy of Colleges
文章编号:
1673-629X(2020)07-0091-05
作者:
李 博1; 2 ; 李 霞1; 3 ; 张 晓1; 2 ; 王艳秋1; 2 ; 李 恒1; 2 ; 张 勇1; 2 ; 凌玉龙1; 2
1. 西北工业大学 计算机学院,陕西 西安 710129; 2. 西北工业大学 工信部大数据存储与管理重点实验室,陕西 西安 710129; 3. 西北工业大学 学生资助服务中心,陕西 西安 710129
Author(s):
LI Bo1; 2 ; LI Xia1; 3 ; ZHANG Xiao1; 2 ; WANG Yan-qiu1; 2 ; LI Heng1; 2 ; ZHANG Yong1; 2 ; LING Yu-long1; 2
1. School of Computer Science,Northwestern Polytechnical University,Xi’an 710129,China; 2. Ministry of Communications Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University,Xi’an 710129,China; 3. Student Aid Service Center,Northwestern Polytechnical University,Xi’an 710129,China
关键词:
MD-KNN 算法; 马氏距离; 高校精准资助; 聚类算法; 数据挖掘
Keywords:
Mahalanobis distance k - nearest neighbor algorithm;  Mahalanobis distance;  precise subsidy of colleges and universities;  clustering algorithm; data mining
分类号:
TP311. 13
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 07. 020
摘要:
精准资助是当前一个热点问题,国内很多高校也对学生精准问题进行了深入的探索。 为提升高校学生精准资助工作的准确性,采用 MD-KNN 算法(Mahalanobis distance k-nearest neighbor algorithm) 对该问题进行分析。 对收集到的数据信息利用基于马氏距离的 MD-KNN 算法进行聚类,再对聚类结果进行迭代分析,以提高经济困难学生筛选工作的精度。 学生群体由于其本身的特殊性,其行为也会与贫困情况有联系,文中对学生行为与贫困情况进行分析:发现学生在学校食堂就餐次数、就餐天数与贫困指数具有正相关的联系。以西安某高校 2017 年 11 月至 2018 年 4 月学生行为数据为样本进行实验;用生成的名单与线下正常认证的贫困学生名单进行对比。 实验证明 MD-KNN 算法在高校学生精准资助中具有很大的应用价值。
Abstract:
Precision subsidy is a hot issue at present. Many colleges in China have explored the problem of students’ precision in depth. In order to improve the accuracy of the precise subsidy work for college students,the MD-KNN algorithm (Mahalanobis distance k-nearest neighbor algorithm) is used to analyze the problem. The collected data are clustered by the MD-KNN algorithm based on Mahalanobis distance, and the clustering results are analyzed iteratively, which improves the accuracy of screening for the students whose families are financially difficult. Because of the particularity of students,their behavior will also be related to poverty. We analyze the connection of students’ behavior and poverty. It is found that the number of times students eat in school canteens,the number of days they eat have a positive correlation with poverty index. The experiments are based on the data of students’ behavior from November 2017 to April 2018 in a university in Xi’an. The results are compared with the list of poverty -stricken students who are normally certified offline. Experiment shows that the MD-KNN algorithm has great application value in the precise subsidy of College students.

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更新日期/Last Update: 2020-07-10