Targeted Mining of Time-Interval Related Patterns
Shuang Liang, Lili Chen, Wensheng Gan, Philip S. Yu, Shengjie Zhao · IEEE Transactions on Neural Networks and Learning Systems · 2025
Compared with frequent pattern mining, sequential pattern mining emphasizes the temporal aspect and finds broad applications across various fields. However, numerous studies treat temporal events as single time points, neglecting their durations. Time-interval-related pattern (TIRP) mining is introduced to address this issue and has been applied to healthcare analytics, stock prediction, etc. Typically, mining all patterns is not only computationally challenging for accurate forecasting but also resource-intensive in terms of time and memory. Targeting the extraction of TIRPs based on specific criteria can improve data analysis efficiency and better align with customer pReferences. Therefore, this article proposes a novel algorithm called TaTIRP to discover targeted TIRP. In addition, we develop multiple pruning strategies to eliminate redundant extension operations, thereby enhancing performance on large-scale datasets. Finally, we conduct experiments on various real-world and synthetic datasets to validate the accuracy and efficiency of the proposed algorithm.