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Partial periodic patterns mining with multiple minimum supports

机译:具有多个最小支持的部分周期模式挖掘

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Partial periodic patterns are commonly seen in real-world applications. Most of the previous approaches set a single minimum support threshold for all the events in a sequence. However using only one minimum support for all events in an event sequence to assume they have similar frequencies is not easy to happen in real-life applications. In this paper, we propose an algorithm which applies the projection-based mechanism and specifies multiple minimum supports to effectively discover appropriate partial periodic patterns. Finally, the experimental result shows the good performance of the proposed approach.
机译:部分周期性模式在现实世界的应用程序中很常见。大多数以前的方法都为序列中的所有事件设置了单个最小支持阈值。但是,在事件应用程序中仅对事件序列中的所有事件仅使用一个最小支持以假定它们具有相似的频率是不容易的。在本文中,我们提出了一种算法,该算法应用基于投影的机制并指定多个最小支持以有效地发现适当的局部周期模式。最后,实验结果表明了该方法的良好性能。

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