MiSA-Miner An Anchor-Driven Missing-Set Framework for Maximal and Closed Frequent Itemset Mining

In frequent itemset mining over large transactional databases, the complete set of frequent itemsets is often extremely large and highly redundant.Therefore, maximal frequent itemsets (MFIs) and closed frequent itemsets (CFIs) are commonly used as more compact result representations. However,existing MFI/CFI mining methods, although designed to enumerate maximal or
closed patterns directly, still often need to maintain and evaluate a large number of intermediate patterns during search and incur substantial maximality/closure checking costs in practice, which can limit mining efficiency. To address this issue, this paper proposes MiSA-Miner, an anchor-driven missing-set framework for MFI/CFI mining. The proposed method adopts the missing-set as the core representation, computes support directly from the cardinality of the union of missing-sets, and uses an anchor set composed of minimal elements to organize the search space and drive depth-first expansion, thereby forming a unified representation-and-search framework. For CFI mining, we further present an exact closure-checking method based on missing-sets, so that frequency checking and closure checking can be expressed in a unified representation framework. Experimental results show that, on dense transactional databases under relatively high support thresholds, MiSA-Miner produces MFI results consistent with classical baseline algorithms while achieving significant runtime advantages: compared with FPMax/GenMax, it attains approximately 2.0–36.3× speedup, and in some lower-threshold settings where baseline algorithms fail to finish within a 3-hour time limit (TLE), MiSA-Miner can still complete the mining task. We also analyze the performance boundary of the proposed method on very large sparse datasets and under low-support settings. Overall, this paper presents a unified framework
for MFI/CFI mining under a complementary missing-set representation, and achieves quantifiable efficiency improvements in target scenarios.