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Stat-DSM: Statistically Discriminative Sub-Trajectory Mining With Multiple Testing Correction
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DOI：10.1109/TKDE.2020.2994344.png)
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We propose a novel statistical approach to evaluate the statistical significance (reliability) of the results from discriminative sub-trajectory mining, which we call Statistically Discriminative Sub-trajectory Mining (Stat-DSM). Given two groups of trajectories, the goal of Stat-DSM is to extract moving patterns in the form of sub-trajectories that occur statistically significantly more often in one group than in the other. An advantage of the proposed method is that the statistical significance of the extracted sub-trajectories are properly controlled in the sense that the probability of finding a falsely discriminative sub-trajectory is smaller than a specified significance threshold alpha (e.g., 0.05), which is crucial when the method is used in scientific or social science studies under noisy environments. Finding such statistically discriminative sub-trajectories from a massive trajectory dataset is both computationally and statistically challenging. In the Stat-DSM method, we address these difficulties by introducing a tree representation of sub-trajectories, and applying an efficient permutation-based statistical inference method to the tree. To the best of our knowledge, Stat-DSM is the first method that provides a statistical approach to quantify the reliability of discriminative sub-trajectory mining results. We illustrate the effectiveness and scalability of the Stat-DSM method by applying it to a real-world dataset containing 1,000,000 trajectories.
Keyword:
Trajectory mining
significant pattern mining
statistical testing
multiple hypothesis testing
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10.4
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6.8K
被引数:
3.2W
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