Mining Top-n Local Outliers in Constrained Spatial Networks
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TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering
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A Framework for Trajectory Clustering
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Unsupervised trajectory sampling
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iBAT: detecting anomalous taxi trajectories from GPS traces
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KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
A novel outlier detection method for spatio-tempral trajectory data
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
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Query answering on trajectory cuboids using prime numbers encodings
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Exploration through enrichment: a visual analytics approach for animal movement
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Efficient and effective query answering for trajectory cuboids
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Effectively grouping trajectory streams
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Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. Even worse, an existing trajectory outlier detection algorithm has limited capability to detect outlying sub-trajectories. In this paper, we propose a novel partition-and-detect framework for trajectory outlier detection, which partitions a trajectory into a set of line segments, and then, detects outlying line segments for trajectory outliers. The primary advantage of this framework is to detect outlying sub-trajectories from a trajectory database. Based on this partition-and-detect framework, we develop a trajectory outlier detection algorithm TRAOD. Our algorithm consists of two phases: partitioning and detection. For the first phase, we propose a two-level trajectory partitioning strategy that ensures both high quality and high efficiency. For the second phase, we present a hybrid of the distance-based and density-based approaches. Experimental results demonstrate that TRAOD correctly detects outlying sub-trajectories from real trajectory data.