Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions
Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions
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DOI：
10.1109/icdmw.2012.130
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
K. Pillai;R. Angryk;J. Banda;M. Schuh;Tim Wylie
中科院分区：
文献类型：
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作者：
K. Pillai;R. Angryk;J. Banda;M. Schuh;Tim Wylie
时空共现模式表示在空间和时间上一起发生的事件类型的子集。在比较以前的工作在这一领域中，我们提出了一个通用的框架，以确定时空同现模式不断发展的时空事件，有类似的陈述。我们还提出了一套措施，以确定时空同现模式，并提出了一个基于Apriori的时空同现挖掘算法，以找到普遍的时空同现模式的扩展空间表示，随着时间的推移而演变。我们评估我们的框架在现实生活中的数据，以证明我们的措施和算法的有效性。我们目前的结果突出了我们的措施在确定时空共现模式的重要性。
Spatio-temporal co-occurring patterns represent subsets of event types that occur together in both space and time. In comparison to previous work in this field, we present a general framework to identify spatio-temporal co occurring patterns for continuously evolving spatio-temporal events that have polygon-like representations. We also propose a set of measures to identify spatio-temporal co-occurring patterns and propose an Apriori-based spatio-temporal co-occurrence mining algorithm to find prevalent spatio-temporal co-occurring patterns for extended spatial representations that evolve over time. We evaluate our framework on real-life data to demonstrate the effectiveness of our measures and the algorithm. We present results highlighting the importance of our measures in identifying spatio-temporal co-occurrence patterns.