Time-interval-related pattern (TIRP) mining algorithms find patterns such as “A starts B” or “A overlaps B”. The discovery of TIRPs is computationally highly demanding. In this work, we introduce a new efficient algorithm for mining TIRPs, called vertTIRP which combines an efficient representation of these patterns, using their temporal transitivity properties to manage them, with a pairing strategy that sorts the temporal relations to be tested, in order to speed up the mining process. Moreover, this work presents a robust definition of the temporal relations that eliminates the ambiguities with other relations when taking into account the uncertainty in the start and end time of the events (epsilon-based approach), and includes two constraints that enable the user to better express the types of TIRPs to be learnt. An experimental evaluation of the method was performed with both synthetic and real datasets, and the results show that vertTIRP requires significantly less computation time than other state-of-the-art algorithms, and is an effective approach.

时间间隔相关模式（TIRP）挖掘算法可找到诸如“ A开始B”或“ A重叠B”之类的模式。TIRP的发现对计算要求很高。在这项工作中，我们引入了一种新的用于挖掘TIRP的有效算法，称为ve​​rtTIRP，该算法结合了这些模式的有效表示，使用它们的时间可传递性属性来管理它们以及一种配对策略，该策略对要测试的时间关系进行排序，以便加快采矿过程。此外，这项工作为时间关系提供了一个强有力的定义，当考虑到事件开始和结束时间的不确定性时，消除了与其他关系的歧义（基于ε的方法），并且包括两个约束条件，使用户能够更好地表达要学习的TIRP的类型。