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A new framework for mining frequent interaction patterns from meeting databases

机译：从会议数据库中挖掘频繁交互模式的新框架

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摘要

Meetings play an important role in workplace dynamics in modern life since their atomic components represent the interactions among human beings. Semantic knowledge can be acquired by discovering interaction patterns from these meetings. A recent method represents meeting interactions using tree data structure and mines interaction patterns from it However, such a tree based method may not be able to capture all kinds of triggering relations among interactions and distinguish same interaction from different participants of different ranks. Hence, it is not suitable to find all interaction patterns such as those about correlated interactions. In this paper, we propose a new framework for mining interaction patterns from meetings using an alternative data structure, namely, weighted interaction flow directed acyclic graph (WIFDAG). Specifically, a WIFDAG captures both temporal and triggering relations among interactions in meetings. Additionally, to distinguish participants from different ranks, we assign weights to nodes in the WIFDAGs. Moreover, we also propose an algorithm called WDAGMeet for mining weighted frequent interaction patterns from meetings represented by the proposed framework. Extensive experimental results are shown to signify the effectiveness of the proposed framework and the mining algorithm built on that framework for mining frequent interaction patterns from meetings.
机译：会议在现代生活中的工作场所动态中起着重要作用，因为它们的原子成分代表着人类之间的相互作用。可以通过发现这些会议的交互模式来获取语义知识。最近的方法表示使用树数据结构满足会议交互并从中挖掘交互模式。但是，这种基于树的方法可能无法捕获交互之间的所有触发关系，并且无法将相同的交互与不同等级的不同参与者区分开。因此，不适合找到所有交互模式，例如与相关交互有关的模式。在本文中，我们提出了一个新的框架，用于使用替代数据结构从会议中挖掘交互模式，即加权交互流有向无环图（WIFDAG）。具体而言，WIFDAG可以捕获会议交互之间的时间关系和触发关系。此外，为了区分不同级别的参与者，我们将权重分配给WIFDAG中的节点。此外，我们还提出了一种称为WDAGMeet的算法，用于从所提出的框架代表的会议中挖掘加权的频繁交互模式。大量的实验结果表明该提议的框架和在该框架上构建的挖掘会议频繁交互模式的算法的有效性。

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