|本期目录/Table of Contents|

[1]陈云超,谢加良,林玲,等.基于改进Apriori算法的高校教育满意度关联规则挖掘[J].集美大学学报(自然科学版),2024,29(4):377-384.
　CHEN Yunchao,XIE Jialiang,LIN Ling,et al.Association Rule Mining of Higher Education Satisfaction Based on Improved Apriori Algorithm[J].Journal of Jimei University,2024,29(4):377-384.
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《集美大学学报（自然科学版）》[ISSN:1007-7405/CN:35-1186/N]

卷:
第29卷
期数:
2024年第4期
页码:
377-384
栏目:
数理科学与信息工程
出版日期:
2024-07-28

文章信息/Info

Title:
Association Rule Mining of Higher Education Satisfaction Based on Improved Apriori Algorithm
作者:
陈云超1; 谢加良1; 2; 林玲1; 2; 刘小辉1; 3
1.集美大学理学院，福建 厦门 361021;2.数字福建大数据建模与智能计算研究所，福建 厦门 361021;3.厦门大学教育研究院，福建 厦门 361005
Author(s):
CHEN Yunchao1;  XIE Jialiang1; 2;  LIN Ling1; 2;  LIU Xiaohui1; 3
(1. School of Science, Jimei University, Xiamen 361021, China;2.Digital Fujian Big Data Modeling and Intelligent Computing Institute, Xiamen 361021, China;3. Institute of Educational Research, Xiamen University，Xiamen 361005, China)
关键词:
高校教育满意度; 数据挖掘; 关联规则; Apriori算法
Keywords:
higher education satisfation;  data mining;  association rules;  apriori algorithm
分类号:
-
DOI:
-
文献标志码:
A
摘要:
针对经典关联规则Apriori算法在大数据集情境下易产生冗余和误导性的关联规则，以及难以确认关键性关联规则等问题，提出支持度—置信度—权重检验系数框架与后项约束的改进Apriori算法。首先，定义相关性系数、提升系数、错误系数并进行证明分析，进而构建权重检验系数；其次，运用主成分分析法，提取指标中的高权重影响因素作为后项，通过后项约束过滤冗余关联信息，从而筛选出更为准确的关键性关联规则。将改进的Apriori算法应用于高校教育满意度调查数据的关联规则挖掘并进行分析对比，实验结果验证了该算法的合理性和有效性。
Abstract:
Due to the problem that the classical association rule Apriori algorithm is prone to produce redundant and misleading association rules in the context of large data sets, and it is difficult to identify key association rules, this paper proposes an improved Apriori algorithm based on the support-confidence weight-test coefficient framework and the post-term constraint.Firstly, the correlation coefficient, lifting coefficient and error coefficient were defined and proved, and then the weight test coefficient was constructed. Secondly, the principal component analysis method is used to extract the influential factors with high weight in the index as the latter term, and the redundant association information is filtered through the latter term constraints, so as to screen out more accurate key association rules.The improved Apriori algorithm is applied to mining association rules of the survey data of higher education satisfaction, and the experimental results verify the rationality and effectiveness of the algorithm.

参考文献/References:

相似文献/References:

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　JIN Zhi-hong,XU Qi,LAN Hui.The Multi-dimensional Data Analysis and Data Mining for Container Multimodal Transportation[J].Journal of Jimei University,2011,16(4):268.
[2]魏德志,王奇光,林丽娜.一种基于数据挖掘的Snort系统的设计与应用[J].集美大学学报(自然科学版),2011,16(5):397.
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备注/Memo

备注/Memo:
更新日期/Last Update: 2024-09-23