基于自动机的频繁事件发现算法的统一视图
A unified view of Automata-based algorithms for Frequent Episode Discovery
摘要：
频繁事件发现框架是时态数据采掘中的一个流行框架，有着广泛的应用。多年来，已经提出了许多不同的发作频率的概念以及用于发作发现的不同算法。在本文中，我们提出了一个统一的观点，所有这样的频率计数算法。我们提出了一个通用的算法，使所有目前的算法是它的特殊情况下，这种统一的观点允许一个获得洞察到不同的频率，我们提出了不同的频率之间的定量关系。我们的统一视图还有助于获得各种算法的正确性证明，正如我们在这里展示的那样。我们还指出，这个统一的观点如何帮助我们考虑推广的算法，使他们可以发现情节一般偏序。
英文摘要：
Frequent Episode Discovery framework is a popular framework in Temporal Data Mining with many applications. Over the years many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper we present a unified view of all such frequency counting algorithms. We present a generic algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies and we present quantitative relationships among different frequencies. Our unified view also helps in obtaining correctness proofs for various algorithms as we show here. We also point out how this unified view helps us to consider generalization of the algorithm so that they can discover episodes with general partial orders.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
