(k, l)-Medians Clustering of Trajectories Using Continuous Dynamic Time Warping

Milutin Brankovic, Kevin Buchin, Koen Klaren, André Nusser, Aleksandr Popov, Sampson Wong

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

10 Citaten (Scopus)


Due to the massively increasing amount of available geospatial data and the need to present it in an understandable way, clustering this data is more important than ever. As clusters might contain a large number of objects, having a representative for each cluster significantly facilitates understanding a clustering. Clustering methods relying on such representatives are called center-based. In this work we consider the problem of center-based clustering of trajectories.

In this setting, the representative of a cluster is again a trajectory. To obtain a compact representation of the clusters and to avoid overfitting, we restrict the complexity of the representative trajectories by a parameter l. This restriction, however, makes discrete distance measures like dynamic time warping (DTW) less suited.

There is recent work on center-based clustering of trajectories with a continuous distance measure, namely, the Fréchet distance. While the Fréchet distance allows for restriction of the center complexity, it can also be sensitive to outliers, whereas averaging-type distance measures, like DTW, are less so. To obtain a trajectory clustering algorithm that allows restricting center complexity and is more robust to outliers, we propose the usage of a continuous version of DTW as distance measure, which we call continuous dynamic time warping (CDTW). Our contribution is twofold:
(1) To combat the lack of practical algorithms for CDTW, we develop an approximation algorithm that computes it.
(2) We develop the first clustering algorithm under this distance measure and show a practical way to compute a center from a set of trajectories and subsequently iteratively improve it.

To obtain insights into the results of clustering under CDTW on practical data, we conduct extensive experiments.
Originele taal-2Engels
SubtitelProceedings of the 28th International Conference on Advances in Geographic Information Systems
RedacteurenChang-Tien Lu, Fusheng Wang, Goce Trajcevski, Yan Huang, Shawn Newsam, Li Xiong
Plaats van productieNew York, NY, United States
UitgeverijAssociation for Computing Machinery, Inc
Aantal pagina's12
ISBN van elektronische versie978-1-4503-8019-5
StatusGepubliceerd - 13 nov. 2020
Evenement28th International Conference on Advances in Geographic Information Systems - Online, Seattle, Verenigde Staten van Amerika
Duur: 3 nov. 20206 nov. 2020


Congres28th International Conference on Advances in Geographic Information Systems
Verkorte titelACM SIGSPATIAL 2020
Land/RegioVerenigde Staten van Amerika
Internet adres


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