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An Algorithm for Spatial Outlier Detection Based on Delaunay Triangulation

机译：基于Delaunay三角测量的空间异常检测算法

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摘要

Spatial outliers represent spatial objects whose non-spatial attributes are significantly different from their spatial neighborhoods'. Spatial outlier detection can provide user with unexpected, interesting and useful spatial patterns for further analysis and has received a lot of attention. However, many existing methods for spatial outlier mining use the k-neighborhood method to determine spatial neighborhood which depends on a priori parameter k, and don't consider spatial autocorrelation. As a result, it usually violates the true situation. So we propose a similarity measurement between spatial objects that based on Delaunay Triangulation (DT_SOF), which captures spatial correlation and spatial neighborhood from the dataset itself. Furthermore, the measure takes in account the local behavior of a spatial object in its neighborhood. Finally, experimentations on a synthetic and a real-world ecological geochemical dataset demonstrate that our approach can effectively detect spatial outliers with a lower disturbance by human intervention.
机译：空间异常值代表其非空间属性与其空间邻居显着不同的空间对象。空间异常检测可以为用户提供意外，有趣和有用的空间模式，以进行进一步分析，并已收到很多关注。然而，许多现有的空间异常挖掘方法使用k邻域方法来确定依赖于先验参数k的空间邻域，并且不考虑空间自相关。结果，它通常违反了真实情况。因此，我们提出了基于Delaunay三角测量（DT_SOF）的空间对象之间的相似性测量，其从数据集本身捕获空间相关和空间邻域。此外，该措施考虑到其邻域中的空间对象的本地行为。最后，对合成和现实世界生态地球化学数据集的实验表明，我们的方法可以有效地检测人类干预较低的扰动空间异常值。

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