SCG '94 Proceedings of the tenth annual symposium on Computational geometry
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Discovery of Periodic Patterns in Spatiotemporal Sequences
IEEE Transactions on Knowledge and Data Engineering
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Computational Geometry: Theory and Applications
Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes
GIScience '08 Proceedings of the 5th international conference on Geographic Information Science
Towards a taxonomy of movement patterns
Information Visualization
Mining Group Movement Patterns for Tracking Moving Objects Efficiently
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 7th ACM international conference on Distributed event-based systems
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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.