Visually driven analysis of movement data by progressive clustering

@article{Rinzivillo2008VisuallyDA,
  title={Visually driven analysis of movement data by progressive clustering},
  author={Salvatore Rinzivillo and Dino Pedreschi and Mirco Nanni and Fosca Giannotti and Natalia V. Andrienko and Gennady L. Andrienko},
  journal={Information Visualization},
  year={2008},
  volume={7},
  pages={225-239}
}
The paper investigates the possibilities of using clustering techniques in visual exploration and analysis of large numbers of trajectories, that is, sequences of time-stamped locations of some moving entities. Trajectories are complex spatio-temporal constructs characterized by diverse non-trivial properties. To assess the degree of (dis)similarity between traiectories, specific methods (distance functions) are required. A single distance function accounting for all properties of trajectories… CONTINUE READING

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