Klcluster: center-based clustering of trajectories

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

doi:10.1145/3347146.3359111

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 proceeding › Conference contribution › Academic › peer-review

16 Citations (Scopus)

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 language	English
Title of host publication	27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
Editors	Farnoush Banaei-Kashani, Goce Trajcevski, Ralf Hartmut Guting, Lars Kulik, Shawn Newsam
Place of Publication	New York
Publisher	Association for Computing Machinery, Inc
Pages	496-499
Number of pages	4
ISBN (Electronic)	978-1-4503-6909-1
DOIs	https://doi.org/10.1145/3347146.3359111
Publication status	Published - 5 Nov 2019
Event	27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - Chicago, IL, United States Duration: 5 Nov 2019 → 8 Dec 2019 http://sigspatial2019.sigspatial.org/

Conference

Conference	27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Abbreviated title	ACM SIGSPATIAL 2019
Country/Territory	United States
City	Chicago, IL
Period	5/11/19 → 8/12/19
Internet address	http://sigspatial2019.sigspatial.org/

Keywords

Algorithms and Data Structures
Clustering
Computational Geometry
Trajectories

Access to Document

10.1145/3347146.3359111

Cite this

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). Association for Computing Machinery, Inc. https://doi.org/10.1145/3347146.3359111

Buchin, Kevin A. ; Driemel, Anne ; van de L'Isle, N.A.F. et al. / Klcluster : center-based clustering of trajectories. 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019. editor / Farnoush Banaei-Kashani ; Goce Trajcevski ; Ralf Hartmut Guting ; Lars Kulik ; Shawn Newsam. New York : Association for Computing Machinery, Inc, 2019. pp. 496-499

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title = "Klcluster: center-based clustering of trajectories",

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.",

keywords = "Algorithms and Data Structures, Clustering, Computational Geometry, Trajectories",

author = "Buchin, {Kevin A.} and Anne Driemel and {van de L'Isle}, N.A.F. and Andr{\'e} Nusser",

year = "2019",

month = nov,

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doi = "10.1145/3347146.3359111",

language = "English",

pages = "496--499",

editor = "Farnoush Banaei-Kashani and Goce Trajcevski and Guting, {Ralf Hartmut} and Lars Kulik and Shawn Newsam",

booktitle = "27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019",

publisher = "Association for Computing Machinery, Inc",

address = "United States",

note = "27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2019 ; Conference date: 05-11-2019 Through 08-12-2019",

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Buchin, KA, Driemel, A, van de L'Isle, NAF & Nusser, A 2019, Klcluster: center-based clustering of trajectories. in F Banaei-Kashani, G Trajcevski, RH Guting, L Kulik & S Newsam (eds), 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019. Association for Computing Machinery, Inc, New York, pp. 496-499, 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, IL, United States, 5/11/19. https://doi.org/10.1145/3347146.3359111

Klcluster: center-based clustering of trajectories. / Buchin, Kevin A.; Driemel, Anne; van de L'Isle, N.A.F. et al.
27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019. ed. / Farnoush Banaei-Kashani; Goce Trajcevski; Ralf Hartmut Guting; Lars Kulik; Shawn Newsam. New York: Association for Computing Machinery, Inc, 2019. p. 496-499.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Klcluster

T2 - 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

AU - Buchin, Kevin A.

AU - Driemel, Anne

AU - van de L'Isle, N.A.F.

AU - Nusser, André

PY - 2019/11/5

Y1 - 2019/11/5

N2 - 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.

AB - 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.

KW - Algorithms and Data Structures

KW - Clustering

KW - Computational Geometry

KW - Trajectories

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U2 - 10.1145/3347146.3359111

DO - 10.1145/3347146.3359111

M3 - Conference contribution

SP - 496

EP - 499

BT - 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019

A2 - Banaei-Kashani, Farnoush

A2 - Trajcevski, Goce

A2 - Guting, Ralf Hartmut

A2 - Kulik, Lars

A2 - Newsam, Shawn

PB - Association for Computing Machinery, Inc

CY - New York

Y2 - 5 November 2019 through 8 December 2019

ER -

Buchin KA, Driemel A, van de L'Isle NAF, Nusser A. Klcluster: center-based clustering of trajectories. In Banaei-Kashani F, Trajcevski G, Guting RH, Kulik L, Newsam S, editors, 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019. New York: Association for Computing Machinery, Inc. 2019. p. 496-499 doi: 10.1145/3347146.3359111

Klcluster: center-based clustering of trajectories

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