Dynamic Algorithm for Mining Relevant Association Rules via Meta-Patterns and Refinement-Based Measures

The mining of relevant association rules from transactional databases is a fundamental process in data mining. Traditional algorithms, however, will typically be based on fixed thresholds and general rule generation, with the result being large and redundant outcomes. This paper presents DERAR (Dynamic Extracting of Relevant Association Rules), a dynamic approach integrating structure pattern mining and dynamic multi-criteria filtering. The process begins with the generation of frequent meta-patterns. Each entity is given a stability score for its consistency across various data projections, then sorted by mutual information in order to preserve the most informative dimensions. The resulting association rules from these models are filtered through a dynamic confidence threshold that is adjusted according to the statistical distribution of the dataset. A final semantic filtering phase identifies rules with high coherence between antecedent and consequent. Experimental results show that DERAR reduces rules by up to 85%, improving interpretability and coherence. It outperforms Apriori, FP-Growth, and H-Apriori in rule quality and computational efficiency. DERAR consistently achieves lower execution times and memory use, especially on large or sparse datasets. These results confirm the benefits of adaptive, semantically guided rule mining for generating concise, high-quality, and actionable knowledge.