Mining frequent closed patterns is a fundamental task in both frequent pattern mining and association rule mining. In this research, we propose two efficient algorithms, tTRK-CloITP and dTRK-CloITP, for mining top‑rank‑k frequent closed inter‑transaction patterns (FCITPs) using tidset and diffset structures, respectively. Our approach offers several key contributions. First, the database is scanned once to generate 1‑patterns along with their corresponding tidsets. From this set of 1‑patterns, inter‑transaction 2‑patterns are generated using either the tidset or diffset structure without requiring another scan of the entire mega‑transaction database. This enables efficient inter‑transaction pattern generation starting from the 2‑pattern level. A depth‑first search (DFS) traversal is then used to iteratively generate (l + 1)‑patterns from l‑patterns. Second, we introduce additional pruning techniques based on either the tidset or diffset structure to efficiently identify closed patterns when inserting them into the top‑rank‑k table, which contains the complete set of top‑rank‑k frequent closed inter‑transaction patterns. Finally, we conduct extensive experiments on benchmark datasets from the FIMI repository ( http://fimi.uantwerpen.be/data/ ) to demonstrate the effectiveness and efficiency of the proposed algorithms in terms of both runtime and memory usage.