Abstract Discovery of localized and irregularly shaped anomalous patterns in spatial data provides useful context for operational decisions across many policy domains. The support vector subset scan (SVSS) integrates the penalized fast subset scan with a kernel support vector machine classifier to accurately detect spatial clusters without imposing hard constraints on the shape or size of the pattern. The method iterates between (1) efficiently maximizing a penalized log-likelihood ratio over subsets of locations to obtain an anomalous pattern, and (2) learning a high-dimensional decision boundary between locations included in and excluded from the anomalous subset. On each iteration, location-specific penalties to the log-likelihood ratio are assigned according to distance to the decision boundary, encouraging patterns which are spatially compact but potentially highly irregular in shape. SVSS outperforms competing methods for spatial cluster detection at the task of detecting randomly generated patterns in simulated experiments. SVSS enables discovery of practically-useful anomalous patterns for disease surveillance in Chicago, IL, crime hotspot detection in Portland, OR, and pothole cluster detection in Pittsburgh, PA, as demonstrated by experiments using publicly available data sets from these domains.

中文翻译：

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用于空间模式检测的支持向量子集扫描

摘要 空间数据中局部和不规则形状异常模式的发现为跨多个政策领域的运营决策提供了有用的背景。支持向量子集扫描 (SVSS) 将惩罚快速子集扫描与内核支持向量机分类器相结合，以准确检测空间集群，而无需对模式的形状或大小施加硬约束。该方法在（1）有效地最大化位置子集上的惩罚对数似然比以获得异常模式，和（2）学习异常子集中包含和排除的位置之间的高维决策边界之间进行迭代。在每次迭代中，根据与决策边界的距离分配对数似然比的特定位置惩罚，鼓励空间紧凑但形状可能高度不规则的图案。在检测模拟实验中随机生成的模式的任务中，SVSS 优于空间集群检测的竞争方法。SVSS 能够发现实用的异常模式，用于伊利诺伊州芝加哥的疾病监测、俄勒冈州波特兰的犯罪热点检测以及宾夕法尼亚州匹兹堡的坑洼集群检测，如使用来自这些领域的公开可用数据集的实验所证明的那样。