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Trajectory Outlier Detection Based on Multi-Factors
Lei ZHANG Zimu HU Guang YANG
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2014/08/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Data Engineering, Web Information Systems
trajectory, outlier detection, multi-factors, canonical correlation analysis,
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Most existing outlier detection algorithms only utilized location of trajectory points and neglected some important factors such as speed, acceleration, and corner. To address this problem, we present a Trajectory Outlier Detection algorithm based on Multi-Factors (TODMF). TODMF is improved in terms of distance-based outlier detection algorithms. It combines multi-factors into outlier detection to find more meaningful trajectory outliers. We resort to Canonical Correlation Analysis (CCA) to optimize the number of factors when determining what factors will be considered. Finally, the experiments with real trajectory data sets show that TODMF performs efficiently and effectively when applied to the problem of trajectory outlier detection.