Klcluster: center-based clustering of trajectories

Kevin A. Buchin, Anne Driemel, N.A.F. van de L'Isle, André Nusser

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

2 Citaten (Scopus)

Samenvatting

Center-based clustering, in particular k-means clustering, is frequently used for point data. Its advantages include that the resulting clustering is often easy to interpret and that the cluster centers provide a compact representation of the data. Recent theoretical advances have been made in generalizing center-based clustering to trajectory data. Building upon these theoretical results, we present practical algorithms for center-based trajectory clustering.
Originele taal-2Engels
Titel27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
RedacteurenFarnoush Banaei-Kashani, Goce Trajcevski, Ralf Hartmut Guting, Lars Kulik, Shawn Newsam
Plaats van productieNew York
UitgeverijAssociation for Computing Machinery, Inc
Pagina's496-499
Aantal pagina's4
ISBN van elektronische versie978-1-4503-6909-1
DOI's
StatusGepubliceerd - 5 nov 2019
Evenement27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - Chicago, IL, Verenigde Staten van Amerika
Duur: 5 nov 20198 dec 2019
http://sigspatial2019.sigspatial.org/

Congres

Congres27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Verkorte titelACM SIGSPATIAL 2019
LandVerenigde Staten van Amerika
StadChicago, IL
Periode5/11/198/12/19
Internet adres

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  • Citeer dit

    Buchin, K. A., Driemel, A., van de L'Isle, N. A. F., & Nusser, A. (2019). Klcluster: center-based clustering of trajectories. In F. Banaei-Kashani, G. Trajcevski, R. H. Guting, L. Kulik, & S. Newsam (editors), 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019 (blz. 496-499). Association for Computing Machinery, Inc. https://doi.org/10.1145/3347146.3359111