Discovering periodic patterns consists of identifying all sets of items (values) that periodically co-occur in a discrete sequence. Although traditional periodic pattern mining algorithms have multiple applications, they have two key limitations. First, they consider that a pattern is not periodic if the time difference between two of its successive occurrences is greater than a maxPer threshold. But this constraint is too strict, as a pattern may be discarded based on only two of its occurrences, although it may be usually periodic. Second, traditional algorithms use a constraint that the support (occurrence frequency) of a pattern must be no less than a minSup threshold. But setting that parameter is not intuitive. Hence, it is usually set by trial and error, which is time-consuming. This paper addresses the first limitation by introducing a concept of stability to find periodic patterns that have a stable periodic behavior. Then, the second limitation is addressed by proposing an algorithm named TSPIN (Top-k Stable Periodic pattern mINer) to find the top-k stable periodic patterns, where the user can directly specify the number of patterns k to be found rather than using the minSup threshold. Several experiments have been performed to assess TSPIN’s performance, and it was found that it is efficient and can discover patterns that reveal interesting insights in real data.

发现周期性模式包括识别离散序列中周期性同时出现的所有项目（值）集。尽管传统的周期性模式挖掘算法有多种应用，但它们有两个主要限制。首先，他们认为，如果两次连续出现之间的时间差大于m a x P e r阈值，则该模式不是周期性的。但是这种限制太严格了，因为虽然模式通常可能是周期性的，但可能仅基于模式的两次出现就将其丢弃。第二，传统的算法使用约束的图案的支撑体（出现频率）必须不小于一米我Ñ小号ù p阈。但是设置该参数并不直观。因此，通常由反复试验来设置，这很费时。本文通过引入稳定性概念来发现具有稳定周期性行为的周期性模式，从而解决了第一个限制。然后，通过提出一种名为TSPIN（Top-k稳定周期模式mINer）的算法以找到top-k稳定周期模式来解决第二个局限，用户可以直接指定要找到的模式k的数量，而不必使用米我ñ小号Ú p阈值。已经进行了一些实验来评估TSPIN的性能，并且发现它是有效的，并且可以发现揭示真实数据有趣观点的模式。