Cluster-based trajectory segmentation with local noise
  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.
