Journal of Applied Science and Engineering

Published by Tamkang University Press

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An Apriori-BN Hybrid Model for Mining Association Rules in Teaching Data

 2026

 Volume 33

Wei Zhu

Yellow River Conservancy Technical University, Kaifeng, Henan 475004, China

Received: April 1, 2026
Accepted: April 17, 2026
Publication Date: June 27, 2026

上傳圖片

Effect of Data Sparsity on Average Lift 

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This paper proposes an Apriori-Bayesian Network hybrid model for mining association rules in teaching evaluation data. A total of 5,200 teaching evaluation records were cleaned, discretized, and transformed into transaction data. Apriori was first used to generate frequent itemsets under sensitivity-tested support and onfidence thresholds, and Bayesian network inference was then introduced to reduce redundant rules and improve rule interpretability. Experimental results show that the proposed model achieves higher rule quality than Apriori, FP-Growth, Eclat, and genetic association rule mining. The average rule redundancy is reduced by 18.7%, and the key rules obtain lift values greater than 1.30, indicating strong positive associations. The model also supports rule-based warning, performance prediction, and teaching quality diagnosis in a B/S management system. The results demonstrate that the proposed method is effective for teaching data mining and decision support.

Keywords: Data mining; Association rules; Teaching quality; Bayesian network

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