Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey
Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey
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DOI：
10.1145/3487893
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发表时间：
2023-03-01
影响因子：
16.6
通讯作者：
Li，Yan
中科院分区：
文献类型：
--
作者：
Xie，Yiqun;Shekhar，Shashi;Li，Yan
绘制空间热点，即，具有显著更高的某些事件的情况生成率的区域（例如，疾病或犯罪案件），是包括公共卫生、公共安全、交通、农业、环境科学等在内的不同社会领域中的重要任务。由于虚假结果的高经济和社会成本（例如，犯罪集群的虚假警报）。因此，需要明确的统计严格性来控制虚假检测的比率。为了解决这一挑战，用于预测鲁棒聚类的技术（例如，扫描统计）已经被数据挖掘和统计社区广泛研究。在这次调查中，我们提出了一个最新的和详细的审查模型和算法开发的这一领域。首先，我们提出了一个通用的分类法，强大的聚类，包括数据和统计建模，区域枚举和最大化，和显着性测试的关键步骤。我们进一步讨论每个关键步骤中的不同范式和方法。最后，我们强调了研究差距和潜在的未来方向，这可能是在这个不断增长的领域和超越产生新的想法和想法的垫脚石。
Mapping of spatial hotspots, i.e., regions with significantly higher rates of generating cases of certain events (e.g., disease or crime cases), is an important task in diverse societal domains, including public health, public safety, transportation, agriculture, environmental science, and so on. Clustering techniques required by these domains differ from traditional clustering methods due to the high economic and social costs of spurious results (e.g., false alarms of crime clusters). As a result, statistical rigor is needed explicitly to control the rate of spurious detections. To address this challenge, techniques for statistically-robust clustering (e.g., scan statistics) have been extensively studied by the data mining and statistics communities. In this survey, we present an up-to-date and detailed review of the models and algorithms developed by this field. We first present a general taxonomy for statistically-robust clustering, covering key steps of data and statistical modeling, region enumeration and maximization, and significance testing. We further discuss different paradigms and methods within each of the key steps. Finally, we highlight research gaps and potential future directions, which may serve as a stepping stone in generating new ideas and thoughts in this growing field and beyond.