Stat-DSM: Statistically Discriminative Sub-Trajectory Mining With Multiple Testing Correction
To the best of the knowledge, Stat-DSM is the first method that provides a statistical approach to quantify the reliability of discriminative sub-trajectory mining results.
Abstract
We propose a novel <italic>statistical approach</italic> to evaluate the <italic>statistical significance</italic> (reliability) of the results from discriminative sub-trajectory mining, which we call <italic>Statistically Discriminative Sub-trajectory Mining (Stat-DSM)</italic>. 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 <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math><alternatives><mml:math><mml:mi>α</mml:mi></mml:math><inline-graphic xlink:href="vo-ieq1-2994344.gif"/></alternatives></inline-formula> (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.