@article{Ge2026, 
author = {Yan Ge and Zhenzhou Zhang and Chien-Ming Chen},
title = {Mining High-Quantitative Periodic Frequent Patterns across Multiple Sequences},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {88},
number = {2},
pages = {38},
keywords = {Data mining, high-quantitative periodic patterns, multi-sequence databases, quantitative pattern mining},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.077790},
doi = {10.32604/cmc.2026.077790},
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.}
}