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

Abstract

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

CLC Number:

TP311

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.

Figures/Tables 5

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Table 1

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References 15

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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

Accurate funding method for student assistance system based on improved Apriori algorithm

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