Abstract:
We propose Stat-DSM to assess the statistical significance (reliability) of discriminative sub-trajectory mining results. Stat-DSM extracts sub-trajectories that occur statistically significantly more often in one group than the other from two groups of trajectories.
The proposed method controls statistical significance by ensuring that the probability of finding a falsely discriminative sub-trajectory is less than a specified significance threshold α (e.g., 0.05), which is important in noisy scientific or social science studies.
Finding statistically discriminative sub-trajectories from a massive trajectory dataset is computationally and statistically difficult. We use a tree representation of sub-trajectories and an efficient permutation-based statistical inference method to solve these problems in Stat-DSM.
Stat-DSM is the first statistical method to quantify discriminative sub-trajectory mining reliability, to our knowledge. Applying Stat-DSM to a real-world dataset of 1,000,000 trajectories shows its efficacy and scalability.
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