Abstract
The improvement in mobile computing techniques has generated massive trajectory data, which represent the mobility of moving objects like vehicles, animals, and people. Mining trajectory data and especially outlier detection in trajectory data is an attractive and challenging topic that fascinated many researchers. In this paper, we propose a Clustering-Based Trajectory Outlier Detection algorithm (CB-TOD). The proposed algorithm partitions a trajectory into line segments and decreases those line segments to a smaller set (Summary-trajectory SS(t)) without affecting the spatial properties of the original trajectory. After that the CB-TOD algorithm using a clustering method to detect the cluster with the smallest number of segments for a trajectory and a small number of neighbors to be sub-trajectory outliers for this trajectory. Also, our proposed algorithm can detect outlier trajectories in the dataset. The main advantage of CB-TOD algorithm is reducing the computational time for outlier detection especially for big trajectory data without affecting the efficiency of the outlier detection results. Experimental results demonstrate that CB-TOD outperforms the state of art existing algorithms in identifying outlier sub-trajectories and also outlier trajectories in real trajectory dataset.
Highlights
The various advances in GPS devices supported collecting an enormous number of moving objects data and rapidly
Outlier detection in data mining relates to identifying an object that is incompatible with the other objects [1]
In this part of the experiments, we evaluate the run-time of the proposed algorithm (CB-Trajectory Outlier Detection (TOD))
Summary
The various advances in GPS devices supported collecting an enormous number of moving objects data and rapidly. Identifying moving objects trends which may be events, represented by a group of animal moving objects in a specific time that does not conform to a familiar pattern, is essential for detecting animal abnormal habit and attracts the attention of biologists[6] These applications are behind our motivation work presented in this paper. The main contributions in this paper are the following: We employed a novel model that reduces the computational time by decreasing the size of the trajectories dataset and representing each trajectory with the Summary set of line segments that are adequate to define the trajectory behavior without missing the basic motion information. Experimental results are presented and demonstrate that CB-TOD outperforms existing algorithms in detecting both outlying sub-trajectories and outlier trajectories for real trajectory data.
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