Unsupervised Trajectory Sampling

Abstract

A novel methodology for efficiently sampling Trajectory Databases (TD) for mobility data mining purposes is presented. In particular, a three-step unsupervised trajectory sampling methodology is proposed, that initially adopts a symbolic vector representation of a trajectory which, using a similarity-based voting technique, is transformed to a continuous function that describes the representativeness of the trajectory in the TD. This vector representation is then relaxed by a merging algorithm, which identifies the maximal representative portions of each trajectory, at the same time preserving the space-time mobility pattern of the trajectory. Finally, a novel sampling algorithm operating on the previous representation is proposed, allowing us to select a subset of a TD in an unsupervised way encapsulating the behavior (in terms of mobility patterns) of the original TD. An experimental evaluation over synthetic and real TD demonstrates the efficiency and effectiveness of our approach.