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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Research on Automatic Mining Method of Behavior Rule Based on Apriori Algorithm
ZHU Aiqun, LU Jin
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DOI:10.17265/2161-623X/2026.05.004
Shenzhen City Polytechnic, Shenzhen, China; Shenzhen Polytechnic University, Shenzhen, China
Behaviour rule mining extracts valuable patterns from large amounts of behavioural data, which is crucial for analysing user behaviour, monitoring systems, and detecting security threats. Traditional manual or statistical methods often fail to reveal complex, hidden associations. This study therefore proposes an automated behaviour rule mining method based on an improved Apriori algorithm. This method adapts data preprocessing and feature encoding to behavioural characteristics, introduces an adaptive support threshold and incremental updating to enhance efficiency, and automates the generation and filtering of association rules from frequent behavioural sequences. When evaluated using accuracy, recall, and interpretability metrics on public user behaviour data and simulated system logs, the method was found to effectively mine meaningful rules while maintaining high efficiency with large-scale data. This work offers a scalable and interpretable approach to automated behaviour rule mining that supports intelligent analysis and decision-making.
Apriori algorithm, behavior mining, frequent patterns, association rules, automated analysis, data mining
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