Mining group movement patterns
Udo Feuerhake, Monika Sester · 2013
In this paper we aim to recognize a priori unknown group movement patterns. We propose a constellation-based approach to extract repetitive relative movements of a constant group, which are allowed to be rotated, translated or differently scaled. To this end, we record a sequence of constellations, which are used for describing the movements relatively. We deal with uncertainties, and similarities of constellations respectively, by clustering the constellations. Further, we have developed a sequence mining algorithm, which uses the clustering results and tree-like data structures to extract the requested patterns from the sequence. Finally, this approach is applied to different datasets containing real trajectory data provided by different tracking devices. By this way, we want to show its portability to different use cases.