Dynamic Algorithm for Mining Relevant Association Rules via Meta-Patterns and Refinement-Based Measures
Year: 2025
Type: article
Abstract: 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 DMAR (Dynamic Mining of 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. Experiments conducted over a range of benchmark datasets reveal that DMAR consistently reduces the number of rules mined while also improving their clarity, relevance, and non-redundancy. In comparison to standard methods, this approach derives more concise and comprehensible rule sets. An adaptive approach such as this is particularly useful for large or interactive analytical settings, where concentrated and meaningful knowledge is essential to support decision-making.
Source: Information
DOI: 10.3390/info16060438
Authors: Houda Essalmi, Anass El Affar
Institution: Sidi Mohamed Ben Abdellah University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4410774355