离群值
弹道
计算机科学
异常检测
修剪
数据库扫描
人工智能
集合(抽象数据类型)
模式识别(心理学)
算法
数据挖掘
聚类分析
生物
程序设计语言
物理
树冠聚类算法
相关聚类
农学
天文
作者
Youcef Djenouri,Djamel Djenouri,Jerry Chun‐Wei Lin
出处
期刊:ACM Transactions on Knowledge Discovery From Data
[Association for Computing Machinery]
日期:2021-02-10
卷期号:15 (2): 1-28
被引量:37
摘要
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.
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