Association Rule Mining across Multiple Domains: Systematic Literature Review
DOI:
10.33395/sinkron.v9i4.15227Keywords:
Association Rule Mining, Apriori, FP-Growth, ECLAT, Data Mining, Multi-domain Applications, Systematic Literature ReviewAbstract
This Systematic Literature Review (SLR) synthesizes 50 studies published between 2020 and 2025 that applied Association Rule Mining (ARM) across multiple domains, using the PRISMA 2020 framework. The review examines application areas, algorithm choices, implementation tools, parameter settings, and emerging trends. Results indicate that transportation and market analysis are the most prominent domains, followed by healthcare, manufacturing, and governance, with smaller contributions from tourism, agriculture, energy, and the environment. Apriori remains the most widely used algorithm due to its simplicity, FP-Growth is preferred for efficiency, and hybrid or modified approaches are adopted to address scalability issues. Python dominates as the primary implementation tool, alongside RapidMiner and R-Studio, with parameter thresholds generally adapted to dataset size and domain-specific needs. The novelty of this review lies in providing a cross-domain synthesis of ARM, filling the gap left by prior reviews that were limited to specific fields or algorithms. This broader perspective reveals temporal trends and recurring challenges, particularly scalability and interpretability, while identifying opportunities such as integration with deep learning, real-time ARM, and cross-domain adaptation. By offering a structured overview of developments in ARM, this study contributes both conceptual insights and practical guidance, serving as a reference for optimizing applications and informing future research directions.
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