Learning trajectory patterns by sequential pattern mining from probabilistic databases

Abstract

In this paper, we use Sequential Pattern Mining from Probabilistic Databases to learn trajectory patterns. Trajectories which are a succession of points are firstly transformed into a succession of zones by grouping points to build the symbolic sequence database. For each zone we estimate a confidence level according to the amount of observations appearing during trajectory in the zone. The management of this confidence allows to reduce efficiently the volume of useful zones for the learning process. Finally, we applied a Sequential Pattern Mining algorithm on this probabilistic databases to bring out typical trajectories.