弹道
质心
计算机科学
背景(考古学)
欧几里德距离
异常检测
星团(航天器)
数据挖掘
人工智能
分割
鉴定(生物学)
公制(单位)
过程(计算)
模式识别(心理学)
地理
考古
程序设计语言
物理
经济
操作系统
生物
植物
运营管理
天文
作者
David Sánchez Pedroche,J. García,Jesús López
标识
DOI:10.1016/j.neucom.2023.126920
摘要
This paper presents a context information extraction process over Automatic Identification System (AIS) real-world ship data, building a system with the capability to extract representative points of a trajectory cluster. With the trajectory cluster, the study proposes the use of trajectory segmentation algorithms to extract representative points of each trajectory and then use the k-means algorithm to obtain a series of centroids over all the representative points. These centroids, combined, form a new representative trajectory of the cluster. This new representative trajectory of the input cluster represents new contextual information extracted from the original set of trajectories, being possible to apply anomaly detection approaches over the new obtained context. The results show a suitable approach with several compression algorithms that are compared with a metric based on the Perpendicular Euclidean Distance.
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