基于中心移动的轨迹离群点检测  

Trajectory Outlier Detection Based on Center Shift

作　　者：杨洪宁 徐文进[1] 杜珍珍 姚佳禹 YANG Hong-Ning;XU Wen-Jin;DU Zhen-Zhen;YAO Jia-Yu(School of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学信息科学技术学院,青岛266061

出　　处：《计算机系统应用》2023年第12期189-196,共8页Computer Systems & Applications

摘　　要：AIS数据是指通过AIS系统获取的船舶运动轨迹信息,对其进行挖掘可以获得船舶的运动模式、航行路线、停靠地点等信息.但其在采集过程中产生的离群点会对聚类等任务造成负面影响,因此对AIS数据挖掘之前需要进行离群点检测.然而,当AIS轨迹数据中存在大量离群点时,会导致大多数离群点检测算法的准确率显著下降.为了解决这个问题,本文提出了一种基于中心移动的轨迹离群点检测算法(center shift outlier detection,CSOD).通过迫使数据点向其K近邻集合的中心移动,使每个数据点更加接近典型数据,从而有效地消除了离群点对聚类的影响.为了验证本文算法的有效性,使用浙江海域AIS渔船轨迹数据集,将本文提出的CSOD算法与一些经典的离群点检测算法进行了对比实验.实验结果表明,CSOD算法整体上性能更加优越.AIS data refers to the vessel’s motion trajectory information obtained through the AIS system.Mining AIS data can provide insights into the vessel’s motion patterns,navigation routes,docking locations,etc.However,outliers generated during the AIS data collection can have a negative effect on clustering and other tasks.Therefore,outlier detection on AIS data before mining is necessary.However,when there are a large number of outliers in AIS trajectory data,a significant decrease occurs in the accuracy of most outlier detection algorithms.To address this issue,this study proposes a trajectory outlier detection based on center shift(CSOD).The CSOD algorithm encourages data points to move towards the center of their K-nearest neighbor(KNN)set,making each data point closer to typical data and effectively eliminating the influence of outliers on clustering.To validate the effectiveness of the proposed algorithm,the study conducts comparative experiments between the CSOD algorithm and several classical outlier detection algorithms using the AIS fishing vessel trajectory dataset in the Zhejiang sea area.The experimental results demonstrate that the CSOD algorithm outperforms the other algorithms in terms of overall performance.

关 键 词：AIS数据 离群点检测 聚类 K近邻 异常值得分 

分 类 号：U675.7[交通运输工程—船舶及航道工程] TP311.13[交通运输工程—船舶与海洋工程]