klcluster: Center-based Clustering of Trajectories

被引：21

作者：

Buchin, Kevin ^{[1
]}

Driemel, Anne ^{[2
]}

van de L'Isle, Natasja ^{[1
]}

Nusser, Andre ^{[3
]}

机构：

[1] Eindhoven Tech Univ, Eindhoven, Netherlands

[2] Univ Bonn, Hausdorff Ctr Math, Bonn, Germany

[3] Max Planck Inst Informat, Grad Sch Comp Sci, Saarbrucken, Germany

来源：

27TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2019) | 2019年

关键词：

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

D O I：

10.1145/3347146.3359111

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：496 / 499

页数：4

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