Data Mining and Knowledge Discovery

, Volume 32, Issue 4, pp 1017–1055 | Cite as

Cluster-based trajectory segmentation with local noise

  • Maria Luisa Damiani
  • Fatima Hachem
  • Hamza Issa
  • Nathan Ranc
  • Paul Moorcroft
  • Francesca Cagnacci


We present a framework for the partitioning of a spatial trajectory in a sequence of segments based on spatial density and temporal criteria. The result is a set of temporally separated clusters interleaved by sub-sequences of unclustered points. A major novelty is the proposal of an outlier or noise model based on the distinction between intra-cluster (local noise) and inter-cluster noise (transition): the local noise models the temporary absence from a residence while the transition the definitive departure towards a next residence. We analyze in detail the properties of the model and present a comprehensive solution for the extraction of temporally ordered clusters. The effectiveness of the solution is evaluated first qualitatively and next quantitatively by contrasting the segmentation with ground truth. The ground truth consists of a set of trajectories of labeled points simulating animal movement. Moreover, we show that the approach can streamline the discovery of additional derived patterns, by presenting a novel technique for the analysis of periodic movement. From a methodological perspective, a valuable aspect of this research is that it combines the theoretical investigation with the application and external validation of the segmentation framework. This paves the way to an effective deployment of the solution in broad and challenging fields such as e-science.


Trajectories Segmentation Clustering 



The authors thank Walid Aref, Purdue University, for the discussion on the use of the WARP technique, and Roland Keys, NC Museum of Natural Sciences, for kindly providing the real data used in the experiments.


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Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of MilanMilanItaly
  2. 2.Department of Biodiversity and Molecular EcologyFondazione E. MachSan Michele all’AdigeItaly
  3. 3.Department of Organismic and Evolutionary BiologyHarvard UniversityCambridgeUSA

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