CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Computing longest duration flocks in trajectory data
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Efficient algorithms for mining maximal valid groups
The VLDB Journal — The International Journal on Very Large Data Bases
Computational Geometry: Theory and Applications
Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
On-line discovery of flock patterns in spatio-temporal data
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Mining mobile group patterns: a trajectory-based approach
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Mining multi-object spatial-temporal movement patterns
SIGSPATIAL Special
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Traditionally, a convoy is defined as a set of moving objects that are close to each other for a period of time. Existing techniques, following this traditional definition, cannot find evolving convoys with dynamic members and do not have any monitoring aspect in their design. We propose new concepts of dynamic convoys and evolving convoys, which reflect real-life scenarios, and develop algorithms to discover evolving convoys in an incremental manner.