Analyzing User Trajectories from Mobile Device Data with Hierarchical Dirichlet Processes

  • Negar Ghourchian
  • Doina Precup
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8436)


Mobile devices have become pervasive among users in both work environments as well as everyday life, and they sense a wealth of information that can be exploited for a variety of tasks, such as activity recognition, security or health monitoring. In this paper, we explore the feasibility of trajectory clustering, i.e., detecting similarities between moving objects, for an application related to workplace productivity improvement. We use Hierarchical Dirichlet Processes due to their ability to automatically extract appropriate trajectory segments. The application domain is the analysis of RSSI data, where this machine learning method proves successfully.


Receive Signal Strength Indicator Dynamic Time Warping Anchor Node Dirichlet Process Trajectory Cluster 
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.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Negar Ghourchian
    • 1
  • Doina Precup
    • 1
  1. 1.McGill UniversityCanada

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