Mining High-Quantitative Periodic Frequent Patterns across Multiple Sequences

Periodic pattern mining plays an important role in revealing recurring behavioral regularities from temporal sequence data. Most existing approaches, however, are developed for single-sequence settings and rarely account for quantitative information or sequence-level constraints when patterns recur across multiple sequences. This limits their usefulness in practical scenarios, where a pattern is expected to be not only periodic but also quantitatively significant in a sufficiently large portion of sequences. In this work, we formulate the problem of mining High-Quantitative Periodic Frequent Patterns (HQPFPS) from multi-sequence databases and propose an efficient algorithm, termed MHQPFPS. The proposed method evaluates pattern significance through a quantitative ratio within each sequence and exploits a sequence-level upper bound to effectively prune unpromising candidates during pattern growth. To support efficient evaluation, a compact list-based structure is introduced to maintain support, periodicity, and quantitative statistics, thereby avoiding repeated scans of the database. These components are combined within a depth-first exploration framework to systematically generate valid patterns while discarding those that fail to satisfy the required periodic or quantitative constraints. Experimental results on both real-world and synthetic datasets show that MHQPFPS is able to extract meaningful high-quantitative periodic patterns across multiple sequences. Moreover, the results indicate that the proposed pruning strategies substantially reduce computational cost in terms of runtime and memory consumption under a wide range of parameter settings.