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 denitive 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 eectiveness of the solution is evaluated rst 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 eective deployment of the solution in broad and challenging elds such as e-science.

Damiani, M.L.; Hachem, F.; Issa, H.; Ranc, N.; Moorcroft, P.; Cagnacci, F. (2018). Cluster-based trajectory segmentation with local noise. DATA MINING AND KNOWLEDGE DISCOVERY, 32 (4): 1017-1055. doi: 10.1007/s10618-018-0561-2 handle: http://hdl.handle.net/10449/46578

Cluster-based trajectory segmentation with local noise

Ranc, N.;Cagnacci, F.
Ultimo
2018-01-01

Abstract

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 denitive 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 eectiveness of the solution is evaluated rst 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 eective deployment of the solution in broad and challenging elds such as e-science.
Segmentation
Movement ecology
Movement models
Settore INF/01 - INFORMATICA
2018
Damiani, M.L.; Hachem, F.; Issa, H.; Ranc, N.; Moorcroft, P.; Cagnacci, F. (2018). Cluster-based trajectory segmentation with local noise. DATA MINING AND KNOWLEDGE DISCOVERY, 32 (4): 1017-1055. doi: 10.1007/s10618-018-0561-2 handle: http://hdl.handle.net/10449/46578
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10449/46578
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