Klcluster: center-based clustering of trajectories

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

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.
Original languageEnglish
Title of host publication27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
EditorsFarnoush Banaei-Kashani, Goce Trajcevski, Ralf Hartmut Guting, Lars Kulik, Shawn Newsam
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages496-499
Number of pages4
ISBN (Electronic)978-1-4503-6909-1
DOIs
Publication statusPublished - 5 Nov 2019
Event27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - Chicago, IL, United States
Duration: 5 Nov 20198 Dec 2019
http://sigspatial2019.sigspatial.org/

Conference

Conference27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Abbreviated titleACM SIGSPATIAL 2019
CountryUnited States
CityChicago, IL
Period5/11/198/12/19
Internet address

Keywords

  • Algorithms and Data Structures
  • Clustering
  • Computational Geometry
  • Trajectories

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    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 (Eds.), 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019 (pp. 496-499). New York: Association for Computing Machinery, Inc. https://doi.org/10.1145/3347146.3359111