一种基于DBSCAN聚类算法的船舶异常行为检测方法
An Alternative Metric for Detecting Anomalous Ship Behavior Using a Variation of the DBSCAN Clustering Algorithm
摘要：
人们越来越需要快速准确地识别船舶中的异常行为。本文应用基于密度的噪声空间聚类算法（DBSCAN）的变化，以确定船舶的自动识别系统（AIS）数据的异常行为。DBSCAN算法的这种变化以前已经在文献中介绍过，在这项研究中，我们阐明和探索这种算法的数学细节，并介绍了一种替代的异常度量，这是统计信息比以前建议的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
