vertTIRP: Robust and efficient vertical frequent time interval-related pattern mining
作者：
Highlights：
• A new efficient algorithm for mining Time-Interval-Related Patterns (TIRPs).
• A new transitivity table to allow for reasoning about events with uncertainty in the start and end times.
• A pairing strategy that sorts the temporal relations to be tested to speed up the mining process.
• A robust definition of the temporal relations without ambiguities.
• An inclusion of minimum duration and minimum gap constraints in the TIRP mining field.
摘要
•A new efficient algorithm for mining Time-Interval-Related Patterns (TIRPs).•A new transitivity table to allow for reasoning about events with uncertainty in the start and end times.•A pairing strategy that sorts the temporal relations to be tested to speed up the mining process.•A robust definition of the temporal relations without ambiguities.•An inclusion of minimum duration and minimum gap constraints in the TIRP mining field.
论文关键词：Time Interval Related Patterns,Temporal data mining,Sequential pattern mining,Temporal relations
论文评审过程：Received 28 August 2020, Revised 16 October 2020, Accepted 6 November 2020, Available online 21 November 2020, Version of Record 24 January 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.114276
