Statistically Discriminative Sub-trajectory Mining with Multiple Testing Correction

Vo Nguyen Le Duy, Takuto Sakuma, Taiju Ishiyama, Hiroki Toda, Kazuya Arai, Masayuki Karasuyama, Yuta Okubo, Masayuki Sunaga, Yasuo Tabei, Ichiro Takeuchi

Abstract: We propose a novel statistical approach to evaluate the statistical significance (reliability) of the findings in the discriminative sub-trajectory mining problem, called Statistically Discriminative Sub-trajectory Mining (Stat-DSM). Given two groups of trajectories, the goal 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 Stat-DSM method is that the statistical significance of the extracted sub-trajectories are properly controlled in the sense that the probability of finding a false discriminative sub-trajectory is smaller than a specified significance threshold a (e.g., 0.05). We conduct experiments on real-world datasets to demonstrate the effectiveness of the Stat-DSM method.