@Article{cmc.2026.077790,
AUTHOR = {Yan Ge, Zhenzhou Zhang, Chien-Ming Chen},
TITLE = {Mining High-Quantitative Periodic Frequent Patterns across Multiple Sequences},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27007},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2026.077790}
}