基于改进Apriori算法的学生资助系统精准资助方法

doi:10.13229/j.cnki.jdxbgxb.20230111

摘要/Abstract

摘要：

针对学生资助系统精准资助过程易受冗余数据、虚假数据等问题的影响，导致精准资助准确性较差的问题，提出了基于改进Apriori算法的学生资助系统精准资助方法。首先，采集学生消费数据，并采用扩展树状知识库清洗消费数据中的异常、冗余数据，避免这种数据对精准资助过程产生影响；其次，采用基于正态分布的离群点检测法获得贫困生标签数据；最后，采用改进后的Apriori算法获取学生消费与家庭经济状况之间的关联规则，为贫困学生的认定提供依据，完成学生资助系统的精准资助。实验结果表明：本文方法的贫困学生认定精度高、运行时间短、空间复杂度低。

关键词: 扩展树状知识库, 离群点检测, 贫困生标签数据, 扫描数据库, 关联规则

Abstract:

The precision funding process of the student funding system is susceptible to issues such as redundant data and false data， resulting in poor accuracy of precision funding. Therefore， a study on the precision funding method of the student funding system based on the improved Apriori algorithm is proposed. This method first collects student consumption data and uses an extended tree like knowledge base to clean damaged and redundant data in the consumption data， avoiding the impact of such data on the precise funding process. Secondly， the outlier detection method based on normal distribution is used to obtain the poor students' label data. Finally， the improved Apriori algorithm is used to obtain the association rules between students' consumption and family economic status， which provides the basis for the identification of poor students and completes the precise funding of the student funding system. The experimental results show that the proposed method has high identification accuracy， long running time and low space complexity for poor students.

Key words: extended tree knowledge base, outlier detection, poor student label data, scan database, association rules

中图分类号:

TP311

马琨,王喆,范文波. 基于改进Apriori算法的学生资助系统精准资助方法[J]. 吉林大学学报(工学版), 2023, 53(11): 3246-3252.

Kun MA,Zhe WANG,Wen-bo FAN. Accurate funding method for student assistance system based on improved Apriori algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(11): 3246-3252.

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实验次数	运行时间/s
实验次数	本文方法	文献［3］方法	文献［4］方法
1	20.06	40.14	54.69
2	19.12	42.17	55.64
3	19.04	45.21	54.72
4	19.09	45.29	57.62
5	21.08	45.18	54.66
6	20.02	46.16	54.67
7	19.13	45.21	54.71
8	21.02	44.22	58.54
9	20.05	39.16	54.55
10	19.03	45.23	53.47
11	20.03	43.65	56.87
12	21.04	41.28	60.15
13	19.89	39.88	54.55
14	20.41	41.89	60.52
15	21.06	40.27	57.63

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