In this study, we introduce two methods for mining top-rank-k Frequent Inter-Transaction Patterns (FITPs). The first method relies on the tidset-based structure, whereas the second utilizes the diffset-based structure to mine the extracted patterns. We also present effective pruning strategies to eliminate infrequent patterns early on, thereby narrowing the search space and enhancing the efficiency of the mining process in terms of both runtime and memory consumption. To evaluate the performance and effectiveness of these two approaches, we conducted experiments using various datasets obtained from the FIMI repository ( http://fimi.uantwerpen.be/data/ ).