## 1 Introduction  
In the era of data-driven decision-making, organizations increasingly rely on advanced analytics to extract actionable insights from vast volumes of transactional data. E-commerce platforms, in particular, generate high-dimensional, complex datasets derived from customer purchase histories, browsing behavior, and engagement patterns. Leveraging these data to discover frequent patterns and associations enables critical applications such as real-time recommendation engines, personalized marketing, dynamic inventory management, and automated pricing strategies, all of which are essential for maintaining competitiveness in the rapidly evolving digital marketplace. Extracting frequent patterns and associations from these data supports key applications such as real-time product recommendation systems [1], dynamic pricing strategies [2], and intelligent inventory management [3]. These tasks increasingly rely on scalable, interpretable, and statistically robust pattern mining algorithms that can perform under high-volume transactional loads and evolving user behaviors. Consequently, evaluating the practical trade-offs between popular algorithms like Apriori and FP-Growth becomes essential for real-world deployment. Among the most widely adopted techniques for uncovering hidden relationships in transactional data is association rule mining, a foundational method rooted in market basket analysis [4]. This approach aims to identify co-occurrence relationships between items using key metrics such as support, confidence, and lift to evaluate rule relevance and strength [5]. Two dominant algorithms in this domain are Apriori and FP-Growth. The Apriori algorithm, introduced by [PERSON] and [PERSON] [6], utilizes a breadth-first search strategy and multiple database scans to generate frequent itemsets. In contrast, FP-Growth, proposed by [PERSON] et al. [7], adopts a more scalable divide-and-conquer approach by constructing a compact FP-Tree and mining itemsets without candidate generation. Both algorithms have been widely studied in the data mining literature. However, a clear research gap persists in the empirical, tool-based comparison of Apriori and FP-Growth in real-world e-commerce contexts. Most prior studies have focused on synthetic or small-scale datasets, often emphasizing theoretical differences without validating performance through statistically rigorous experimentation. Moreover, few works investigate how these algorithms perform under varying threshold settings (e.g., minimum support and confidence) or assess practical concerns such as memory efficiency and rule interpretability, key factors for operational deployment in modern e-commerce systems. Additionally, integrated benchmarking using end-to-end data science platforms such as RapidMiner combined with formal statistical validation (e.g., in SPSS) remains rare. This study addresses these limitations by conducting a comprehensive comparative analysis of Apriori and FP-Growth on a real-world e-commerce dataset. We evaluate each algorithm across dimensions including the execution time, memory usage, number of generated rules, and rule strength metrics. By varying the parameter settings and validating the results through descriptive and inferential statistical techniques, this research provides both theoretical insights and practical guidance for data scientists, analysts, and system architects. The key contributions of this study include a practical, tool-based implementation of the Apriori and FP-Growth algorithms using RapidMiner v. Studio 7, the development of a statistically validated performance evaluation framework through SPSS, and the provision of empirical insights into the trade-offs between the two algorithms when applied to real-world conditions.