In Association Rule Mining (ARM), the generation of large volumes of associ- ation rules from complex datasets often presents challenges in terms of scalability, efficiency, and interpretability. This thesis addresses these challenges by developing novel methodologies and data structures tailored to improve the post-mining phase of ARM. Our approach begins with the creation of a specialized data structure to efficiently store and retrieve association rules, enhancing memory efficiency and pro- cessing speed. This data structure is then leveraged to design a robust methodology for substitute item mining, an emerging area that enables the identification of alter- native items based on observed patterns, with potential applications in areas such as inventory management and consumer behavior analysis. Furthermore, to improve the interpretability of ARM results, we propose advanced visualization techniques that utilize the developed data structure, allowing users to effectively explore and understand complex relationships within large rulesets. The effectiveness of these methodologies was evaluated through surveys and case studies, demonstrating sig- nificant improvements in both cognitive load for visualization and alignment with consumer preferences in substitute item identification. This research contributes to the broader field of ARM by providing tools that enhance scalability, interpretabil- ity, and practical applicability, paving the way for more efficient knowledge discovery and decision-making in data-rich environments.