Paper Title:
Tra-DBScan: A Algorithm of Clustering Trajectories
  Abstract

Accompany with fast development of location technology, more and more trajectories datasets are collected on the real applications. So it is something of value in the theory and applied research to mine the clusters from these datasets. In this paper, a trajectory clustering algorithm, called Density-Based Spatial Clustering of Application with noise (Tra-DBSCAN for short), based on DBSCAN that is a classic clustering algorithm. In this framework, each trajectory firstly partitions into sub-trajectories as clustering object, and then line hausdorff distance is used to measure the distance between two sub-trajectories. Next, DBSCAN is introduced to cluster sub-trajectory to form cluster area, and then connecting different moments of clustering area is regarded as trajectory movement patterns. Finally, the experimental results show our framework’s effective.

  Info
Periodical
Chapter
Chapter 8: System Modeling and Simulation
Edited by
Dongye Sun, Wen-Pei Sung and Ran Chen
Pages
4875-4879
DOI
10.4028/www.scientific.net/AMM.121-126.4875
Citation
L. X. Liu, J. T. Song, B. Guan, Z. X. Wu, K. J. He, "Tra-DBScan: A Algorithm of Clustering Trajectories", Applied Mechanics and Materials, Vols. 121-126, pp. 4875-4879, 2012
Online since
October 2011
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Price
$32.00
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