论文标题
使用DBSCAN聚类算法的变体检测异常船行为的替代度量
An Alternative Metric for Detecting Anomalous Ship Behavior Using a Variation of the DBSCAN Clustering Algorithm
论文作者
论文摘要
越来越需要快速，准确地确定船舶中的异常行为。本文在噪声（DBSCAN）算法之间采用了基于密度的空间聚类的变化来识别船舶的自动识别系统（AIS）数据，以识别这种异常行为。 DBSCAN算法的这种差异先前是在文献中引入的，在这项研究中，我们阐明和探讨了该算法的数学细节，并引入了一种替代性异常指标，该指标在统计上比以前建议的更具统计信息。
There is a growing need to quickly and accurately identify anomalous behavior in ships. This paper applies a variation of the Density Based Spatial Clustering Among Noise (DBSCAN) algorithm to identify such anomalous behavior given a ship's Automatic Identification System (AIS) data. This variation of the DBSCAN algorithm has been previously introduced in the literature, and in this study, we elucidate and explore the mathematical details of this algorithm and introduce an alternative anomaly metric which is more statistically informative than the one previously suggested.
