Algorithms for spatial outlier detection
Algorithms for spatial outlier detection
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DOI：
10.1109/icdm.2003.1250986
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发表时间：
2003-11
期刊：
影响因子：
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通讯作者：
Chang-Tien Lu;Dechang Chen;Yufeng Kou
中科院分区：
文献类型：
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作者：
Chang-Tien Lu;Dechang Chen;Yufeng Kou
空间离群值是指其非空间属性值与其邻域值显著不同的空间参考对象。空间异常值的识别可以发现意想不到的，有趣的，有用的空间模式，以进一步分析。现有方法的一个缺点是，当正常对象的邻近区域包含真正的空间离群值时，正常对象往往会被错误地检测为空间离群值。我们提出了一套空间离群点检测算法来克服这个缺点。我们制定了一般的空间离群点检测问题，并设计算法，可以准确地检测空间离群点。此外，使用真实世界的人口普查数据集，我们证明了我们的方法不仅可以避免检测虚假的空间离群值，但也发现了真正的空间离群值忽略了现有的方法。
A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. One drawback of existing methods is that normal objects tend to be falsely detected as spatial outliers when their neighborhood contains true spatial outliers. We propose a suite of spatial outlier detection algorithms to overcome this disadvantage. We formulate the spatial outlier detection problem in a general way and design algorithms which can accurately detect spatial outliers. In addition, using a real-world census data set, we demonstrate that our approaches can not only avoid detecting false spatial outliers but also find true spatial outliers ignored by existing methods.