Trajectory Outlier Detection: A Partition-and-Detect Framework

By Jae-Gil Lee, Jiawei Han, Xiaolei Li. IEEE International Conference on Data Engineering, 2008

492 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ca5dcb06d66743c461e6146bdc8800d58ec64377

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Charu C. Aggarwal
2. Yu Zheng
3. Vipin Kumar
4. Philip S. Yu
5. Zhi-Hua Zhou
6. Sanjay Chawla
7. Anuj Karpatne
8. Guoliang Li

In Computer Vision and Pattern Recognition

1. Yu Zheng
2. Charu C. Aggarwal
3. Xing Xie
4. Vipin Kumar
5. Guoliang Li
6. Haitao Yuan
7. Zhi-Hua Zhou
8. Anuj Karpatne

In Signal Processing

1. Yu Zheng
2. Xing Xie
3. Jing Yuan
4. Guoliang Li
5. Haitao Yuan
6. Gao Cong
7. Charu C. Aggarwal
8. Vipin Kumar

Citations in the corpus

• Outlier Detection for Temporal Data: A Survey (2014, methodology): “[130] propose TRAjectory Outlier Detection (TRAOD) algorithm which consists of two phases: (1) Partitioning Phase and (2) Detection Phase.”
• A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, methodology): “[214] partition each trajectory into a set of line segments and then detect the anomaly by computing the similarity between different sets of line segments.”
• Trajectory Data Mining (2015, background): “[52] proposed a partition-anddetection framework to find anomalous segments of trajectories from a trajectory data set.”
• Spatio-Temporal Data Mining (2017, background): “For example, [Lee et al. 2008] explored varying notions of distances between trajectory sub-sequences (such as perpendicular distance, parallel distance, and angle distance) and identified a trajectory to be anomalous if it had very few neighbors within a certain threshold.”
• A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “The similarity measures are based on two types of information: 1) sub-trajectories [93]; 2) entire trajectories [32, 172].”
• iBAT: detecting anomalous taxi trajectories from GPS traces (2011, methodology): “Recent years have witnessed an increasing interest in trajectory anomaly detection [14, 6, 9], which aims to detect suspicious moving objects automatically.”
• Outlier Analysis (2013, methodology): “Even though the existing work [292] has been designed for spatial trajectories, it can also be extended to the non-spatial case with arbitrary values on the X-coordinates and Y coordinates.”
• TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering (2008, unknown): “The distance function that we use is composed of three components [11, 12]: (i) the perpendicular distance ( d ⊥ ), (ii) the parallel distance ( d (cid:107) ), and (iii) the angle distance ( d θ ).”
• Discovering spatio-temporal causal interactions in traffic data streams (2011, methodology): “[15, 14] have designed a “partition-and-group” framework for clustering and detecting trajectory outliers.”
• Mining Individual Life Pattern Based on Location History (2009, background): “On this topic, some literatures aim at performing traditional data mining tasks on spatial-temporal data, like classification [1], clustering [2], pattern mining [3], [4] and outlier detection [5].”