Mining Co-locations under Uncertainty
Mining Co-locations under Uncertainty
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DOI：
10.1007/978-3-642-40235-7_25
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发表时间：
2013-08
期刊：
影响因子：
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通讯作者：
Zhi Liu;Y. Huang
中科院分区：
文献类型：
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作者：
Zhi Liu;Y. Huang
共址模式表示空间特征的子集，其事件倾向于在空间上接近地定位在一起。研究了共址模式的某些情况。然而，空间特征的位置信息通常是不精确的、聚集的或容易出错的。由于空间的连续性，过度计数是一个主要问题。在不确定的情况下，问题变得更具挑战性。在本文中，我们提出了一个概率参与指数，以衡量co-location模式的基础上著名的可能世界模型。为了避免从所有可能的世界计算参与指数的指数成本，我们证明了一个引理，允许例如中心计数，避免过度计数，并产生相同的结果，使用可能的世界计数。我们使用这个属性来开发高效的挖掘算法。我们观察到，通过代数分析和大量的实验，基于特征树的算法优于不确定的Apriori算法的数量级，不仅为大尺寸的协同定位，但也为数据集具有高水平的不确定性。这是挖掘不确定性协同定位的重要见解。
A co-location pattern represents a subset of spatial features whose events tend to locate together in spatial proximity. The certain case of the co-location pattern has been investigated. However, location information of spatial features is often imprecise, aggregated, or error prone. Because of the continuity nature of space, over-counting is a major problem. In the uncertain case, the problem becomes more challenging. In this paper, we propose a probabilistic participation index to measure co-location patterns based on the well-known possible world model. To avoid the exponential cost of calculating participation index from all possible worlds, we prove a lemma that allows for instance centric counting, avoids over-counting, and produces the same results as using possible world based counting. We use this property to develop efficient mining algorithms. We observed through both algebraic analysis and extensive experiments that the feature tree based algorithm outperforms uncertain Apriori algorithm by an order of magnitude not only for co-locations of large sizes but also for datasets with high level of uncertainty. This is an important insight in mining uncertainty co-locations.