This article introduces two new problems related to trajectory outlier detection: (1) group trajectory outlier (GTO) detection and (2) deviation point detection for both individual and group of trajectory outliers. Five algorithms are proposed for the first problem by adapting DBSCAN , k nearest neighbors (kNN) , and feature selection (FS) . DBSCAN-GTO first applies DBSCAN to derive the micro clusters , which are considered as potential candidates. A pruning strategy based on density computation measure is then suggested to find the group of trajectory outliers. kNN-GTO recursively derives the trajectory candidates from the individual trajectory outliers and prunes them based on their density. The overall process is repeated for all individual trajectory outliers. FS-GTO considers the set of individual trajectory outliers as the set of all features, while the FS process is used to retrieve the group of trajectory outliers. The proposed algorithms are improved by incorporating ensemble learning and high-performance computing during the detection process. Moreover, we propose a general two-phase-based algorithm for detecting the deviation points, as well as a version for graphic processing units implementation using sliding windows. Experiments on a real trajectory dataset have been carried out to demonstrate the performance of the proposed approaches. The results show that they can efficiently identify useful patterns represented by group of trajectory outliers, deviation points, and that they outperform the baseline group detection algorithms.

中文翻译：

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轨迹异常值检测

本文介绍了与轨迹异常检测相关的两个新问题：（1）群体轨迹异常值（GTO）检测(2)偏差点检测对于个人和一组轨迹异常值。针对第一个问题提出了五种算法星展扫描,k 最近邻 (kNN)， 和特征选择（FS）.DBSCAN-GTO首先适用星展扫描推导出微集群, 被认为是潜在的候选人。然后建议基于密度计算度量的剪枝策略来找到轨迹异常值组。kNN-GTO递归地从各个轨迹异常值中导出轨迹候选，并根据它们的密度对其进行修剪。对所有单独的轨迹异常值重复整个过程。FS-GTO 将单个轨迹异常值的集合视为所有特征的集合，而 FS 过程用于检索轨迹异常值组。通过在检测过程中结合集成学习和高性能计算来改进所提出的算法。此外，我们提出了一种基于两阶段的通用算法来检测偏差点，以及使用滑动窗口实现图形处理单元的版本。已经在真实轨迹数据集上进行了实验，以证明所提出方法的性能。