Fast Agglomerative Information Bottleneck Based Trajectory Clustering

  • Yuejun Guo
  • Qing Xu
  • Yang Fan
  • Sheng Liang
  • Mateu Sbert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)


Clustering is an important data mining technique for trajectory analysis. The agglomerative Information Bottleneck (aIB) principle is efficient for obtaining an optimal number of clusters without the direct use of a trajectory distance measure. In this paper, we propose a novel approach to trajectory clustering, fast agglomerative Information Bottleneck (faIB), to speed up aIB by two strategies. The first strategy is to do “clipping” based on the so-called feature space, calculating information losses only on fewer cluster pairs. The second is to select and merge more candidate clusters, reducing iterations of clustering. Remarkably, faIB considerably runs above 10 times faster than aIB achieving almost the same clustering performance. In addition, extensive experiments on both synthetic and real datasets demonstrate that faIB performs better than the clustering approaches widely used in practice.


aIB faIB Trajectory clustering Speedup 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yuejun Guo
    • 1
    • 2
  • Qing Xu
    • 1
  • Yang Fan
    • 1
  • Sheng Liang
    • 1
  • Mateu Sbert
    • 1
    • 2
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.Graphics and Imaging LaboratoryUniversitat de GironaGironaSpain

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