Exploratory Trajectory Clustering with Distance Geometry

  • Andrew T. WilsonEmail author
  • Mark D. Rintoul
  • Christopher G. Valicka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9744)


We present here an example of how a large, multi-dimensional unstructured data set, namely aircraft trajectories over the United States, can be analyzed using relatively straightforward unsupervised learning techniques. We begin by adding a rough structure to the trajectory data using the notion of distance geometry. This provides a very generic structure to the data that allows it to be indexed as an n-dimensional vector. We then do a clustering based on the HDBSCAN algorithm to both group flights with similar shapes and find outliers that have a relatively unique shape. Next, we expand the notion of geometric features to more specialized features and demonstrate the power of these features to solve specific problems. Finally, we highlight not just the power of the technique but also the speed and simplicity of the implementation by demonstrating them on very large data sets.


Feature Vector Minimum Span Tree Collision Avoidance Anomaly Detection Trajectory Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We are grateful to AirNav Systems LLC for providing access to the ASDI data feed. We also applaud Leland McInnes for his highly optimized Python implementation of HDBSCAN.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Andrew T. Wilson
    • 1
    Email author
  • Mark D. Rintoul
    • 1
  • Christopher G. Valicka
    • 1
  1. 1.Sandia National LaboratoriesAlbuquerqueUSA

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