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摘要翻译：
频繁事件发现框架是时态数据挖掘中应用广泛的一种框架。多年来，人们提出了许多关于剧集频率的不同概念，并提出了不同的剧集发现算法。在本文中，我们提出了一个统一的观点，所有这些频率计数算法。我们给出了一个通用算法，使得所有现有的算法都是它的特例。这种统一的观点使人们能够洞察不同的频率，并给出不同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，如我们在这里所示。我们还指出，这种统一的观点如何帮助我们考虑算法的推广，以便他们能够发现具有一般偏序的剧集。
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英文标题：
《A unified view of Automata-based algorithms for Frequent Episode
Discovery》
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作者：
Avinash Achar, Srivatsan Laxman and P. S. Sastry
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最新提交年份：
2010
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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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.
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PDF链接：
https://arxiv.org/pdf/1007.0690
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