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.

翻译：越来越需要快速准确地识别船舶中的异常行为。 本文采用了基于密度的空间聚居噪音( DBSCAN) 算法的变异, 以根据船舶自动识别系统( AIS) 的数据来辨别这种异常行为。 DBSCAN 算法的这种变异先前已在文献中引入, 而在这项研究中, 我们解释并探索了这种算法的数学细节, 并引入了比先前建议的更具有统计信息性的替代异常指标。