Mining spatio-temporal co-location patterns with weighted sliding window
Mining spatio-temporal co-location patterns with weighted sliding window
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DOI：
10.1109/icicisys.2009.5358192
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发表时间：
2009-12
期刊：
影响因子：
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通讯作者：
Feng Qian;Liang Yin;Qinming He;Jiangfeng He
中科院分区：
文献类型：
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作者：
Feng Qian;Liang Yin;Qinming He;Jiangfeng He
空间协同定位模式表示其事件在地理空间中经常位于一起的特征（协同定位）的子集。时空共位（共现）模式挖掘将挖掘任务扩展到了空间和时间的范围。然而，将时间因素嵌入到空间同位模式挖掘过程中是一个微妙的问题。以往的研究要么将时间因素作为一个替代维度，要么简单地对每个时间段进行挖掘。在本文中，我们提出了一个加权滑动窗口模型（WSW模型），该模型将时空事件之间的时间间隔的影响引入到时空协同定位模式的兴趣度量。我们发现，上述两种方法适合我们提出的模型中的两种特殊情况。我们还提出了一个算法（STCP-Miner）来挖掘时空协同定位模式。对合成数据集和真实的世界数据集的实验结果表明，该算法在不同参数下都是比较有效的。
Spatial co-location patterns represent the subsets of features (co-location) whose events are frequently located together in geographic space. Spatio-temporal co-location (co-occurrence) pattern mining extends the mining task to the scope of both space and time. However, embedding the time factor into spatial co-location pattern mining process is a subtle problem. Previous researches either treat the time factor as an alternative dimension or simply carry out the mining process on each time segment. In this paper, we propose a weighted sliding window model (WSW-Model) which introduces the impact of time interval between the spatio-temporal events into the interest measure of the spatio-temporal co-location patterns. We figure out that the aforementioned two approaches fit into the two special cases in our proposed model. We also propose an algorithm (STCP-Miner) to mine spatio-temporal co-location patterns. The experimental evaluation with both the synthetic data sets and a real world data set shows that our algorithm is relatively effective with different parameters.