## 2 The Related Literature and Background
### Emerging Applications and Research Directions  
Association rule mining continues to evolve, finding applications across a wide range of domains. In healthcare, it is used to uncover patterns in diagnoses, treatments, and drug interactions [15]. In the field of cybersecurity, it supports the detection of anomalous behaviors and intrusion patterns within network traffic [16]. Within e-commerce, it plays a crucial role in powering product recommendation systems, enabling cross-selling strategies, and optimizing inventory management [17]. Recent work has also explored the integration of association rule mining with machine learning and deep learning, as well as its scalability within big data environments using frameworks like Apache Spark. Although the previous section provides a comprehensive overview of foundational algorithms like Apriori and FP-Growth, as well as their variants such as Eclat, H-Mine, and Closet+, most existing studies exhibit several limitations. Many focus primarily on theoretical comparisons or rely on synthetic datasets that fail to capture the complexity of real-world scenarios. Additionally, statistically validated performance evaluations are often absent, limiting the reliability of their findings. It is also uncommon for these studies to utilize integrated, tool-based environments--such as RapidMiner combined with SPSS--for empirical benchmarking. Furthermore, few systematically explore how algorithm performance varies under practical parameter constraints, including different support and confidence thresholds. Despite the breadth of algorithmic advancements in association rule mining, a noticeable gap persists in studies that combine real-world e-commerce data with rigorous statistical validation. Many existing works either lack empirical grounding, rely on synthetic datasets, or fail to assess performance using integrated tool-based workflows. Furthermore, comparative studies often omit the nuanced impacts of varying parameter thresholds on algorithmic efficiency and rule quality. This study addresses these limitations by offering a statistically robust, tool-assisted evaluation of Apriori and FP-Growth, providing practitioners with actionable insights under realistic data constraints. Recent work has also explored the integration of association rule mining with machine learning, deep learning, and real-time analytics, particularly in domains such as healthcare, cybersecurity, and e-commerce. For example, [PERSON] et al. [18] proposed a hybridFP-Growth and clustering method to reduce rule redundancy, while [PERSON] and [PERSON] [19] developed DeepARM, a deep learning-guided framework for personalized recommendations. In large-scale data environments, [PERSON] et al. [20] demonstrated the scalability of Spark-based FP-Growth for e-commerce, and [PERSON] et al. [21] introduced adaptive threshold tuning for streaming data. Further cross-domain applications include adverse drug interaction detection in healthcare [22] and insider threat detection in cybersecurity using rule-anomaly hybrid models [23]. Despite these innovations, few studies conduct statistically validated, tool-based comparisons of Apriori and FP-Growth using real-world e-commerce data. Our study contributes to filling this methodological gap. In the e-commerce sector, recent advancements underscore the importance of real-time analytics and cross-channel personalization. For example, deep learning-based recommenders are increasingly used to tailor offerings in real-time [1], while reinforcement learning is being applied to automate dynamic pricing strategies based on demand predictions [2]. Furthermore, intelligent inventory systems now integrate data mining algorithms to optimize stock levels and reduce overhead costs [3]. These developments highlight the growing demand for association rule mining techniques that are not only efficient but also statistically validated and scalable under practical conditions [24; 25].