Kinetic data structures: a state of the art report
WAFR '98 Proceedings of the third workshop on the algorithmic foundations of robotics on Robotics : the algorithmic perspective: the algorithmic perspective
Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithmic issues in modeling motion
ACM Computing Surveys (CSUR)
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Time-focused clustering of trajectories of moving objects
Journal of Intelligent Information Systems
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
A Visual Analytics Approach to Exploration of Large Amounts of Movement Data
VISUAL '08 Proceedings of the 10th international conference on Visual Information Systems: Web-Based Visual Information Search and Management
TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering
Proceedings of the VLDB Endowment
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Visualization of vessel movements
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Pathlet learning for compressing and planning trajectories
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Similarity of polygonal curves in the presence of outliers
Computational Geometry: Theory and Applications
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For a set of "similar" trajectories, a median trajectory is a trajectory that is most like the trajectories in the set, but it need not be a trajectory from the set itself. A recent method composed a median from parts of the set of trajectories, using ideas from homotopy to decide which parts to use. That method has two drawbacks. Firstly, it requires a significant subset of the trajectories to be homotopic, and such a subset may not always exist. Secondly, it sometimes misses relevant parts of trajectories because homotopy does not characterize the shape of the trajectories in all situations. In this paper we present a new approach to overcome these two drawbacks, leading to majority medians. We give results from extensive experiments, which indicate that majority medians are indeed better than homotopic medians.