使用结构化数据和分类器挖掘事件关联

Jinxin Zhao, Xinjun Wang, Zhongmin Yan, Song Wei
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引用次数: 0

摘要

事件是近年来一个被广泛使用的概念。自然语言处理、信息检索等领域都将事件作为基本信息单位进行研究。因此,事件关联的挖掘对我们的研究是非常必要的。它在商业智能和事件间关系研究中起着重要的作用。通常,当事件发生在他人附近或在同一上下文中共同发生时,事件与他人相关联。然而,有一些隐含的关联我们不能仅仅从序列或上下文中挖掘。本文旨在寻找数据集成系统背景下的事件关联。利用数据集成系统的结构化信息,提取实体的背景信息,对事件进行分类。因此,我们将事件分为不同的类别,这使得从事件序列中挖掘统计信息成为可能。此外,我们推广了事件实体之间的关联来预测算法中的隐式关联。通过实验验证了该方法的有效性,结果显示了该方法在商业智能领域的有用信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining Event Associations Using Structured Data and Classifiers
Event is a widely used concept these years. Many areas such as Natural Language Process, Information Retrieval have used event as the basic information unit in their research. So, the mining of event association is very necessary for our research. And it plays an important role business intelligence and researches of relations between events. Usually events are associated with others when they often occur in the vicinity of others or co-occur in the same context. However, there are some implicit associations we cannot mine only from sequence or context. In this paper, we aim to find associations of events under the background of Data Integration Systems. By using the structured information of data integration system, the background information of entities can be extracted to classify events. So we classify the events into different categories which makes it possible to mine the statistical information from event sequence. Furthermore, we generalize the association between event entities to predict the implicit association in our algorithm. We validate our method with experiments and results show the useful information in the area of business intelligence.
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