Statistically Discriminative Sub-trajectory Mining
Abstract
We study the problem of discriminative sub-trajectory mining. Given two groups of trajectories, the goal of this problem is to extract moving patterns in the form of sub-trajectories which are more similar to sub-trajectories of one group and less similar to those of the other. We propose a new method called Statistically Discriminative Sub-trajectory Mining (SDSM) for this problem. An advantage of the SDSM method is that the statistical significance of the extracted sub-trajectories are properly controlled in the sense that the probability of finding a false positive sub-trajectory is smaller than a specified significance threshold alpha (e.g., 0.05), which is indispensable when the method is used in scientific or social studies under noisy environment. Finding such statistically discriminative sub-trajectories from massive trajectory dataset is both computationally and statistically challenging. In the SDSM method, we resolve the difficulties by introducing a tree representation among sub-trajectories and running an efficient permutation-based statistical inference method on the tree. To the best of our knowledge, SDSM is the first method that can efficiently extract statistically discriminative sub-trajectories from massive trajectory dataset. We illustrate the effectiveness and scalability of the SDSM method by applying it to a real-world dataset with 1,000,000 trajectories which contains 16,723,602,505 sub-trajectories.
Cite
@article{arxiv.1905.01788,
title = {Statistically Discriminative Sub-trajectory Mining},
author = {Vo Nguyen Le Duy and Takuto Sakuma and Taiju Ishiyama and Hiroki Toda and Kazuya Nishi and Masayuki Karasuyama and Yuta Okubo and Masayuki Sunaga and Yasuo Tabei and Ichiro Takeuchi},
journal= {arXiv preprint arXiv:1905.01788},
year = {2019}
}