A visual-numeric approach to clustering and anomaly detection for trajectory data

Kumar, Dheeraj, Bezdek, James C., Rajasegarar, Sutharshan, Leckie, Christopher and Palaniswami, Marimuthu 2017, A visual-numeric approach to clustering and anomaly detection for trajectory data, Visual computer, vol. 33, no. 3, pp. 265-281, doi: 10.1007/s00371-015-1192-x.

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Title A visual-numeric approach to clustering and anomaly detection for trajectory data
Author(s) Kumar, Dheeraj
Bezdek, James C.
Rajasegarar, SutharshanORCID iD for Rajasegarar, Sutharshan orcid.org/0000-0002-6559-6736
Leckie, Christopher
Palaniswami, Marimuthu
Journal name Visual computer
Volume number 33
Issue number 3
Start page 265
End page 281
Total pages 17
Publisher Springer
Place of publication Berlin, Germany
Publication date 2017-03
ISSN 0178-2789
Keyword(s) trajectory clustering
anomaly detection
ClusiVAT hierarchical clustering
MIT trajectory dataset
Science & Technology
Computer Science, Software Engineering
Computer Science
Summary This paper proposes a novel application of Visual Assessment of Tendency (VAT)-based hierarchical clustering algorithms (VAT, iVAT, and clusiVAT) for trajectory analysis. We introduce a new clustering based anomaly detection framework named iVAT+ and clusiVAT+ and use it for trajectory anomaly detection. This approach is based on partitioning the VAT-generated Minimum Spanning Tree based on an efficient thresholding scheme. The trajectories are classified as normal or anomalous based on the number of paths in the clusters. On synthetic datasets with fixed and variable numbers of clusters and anomalies, we achieve 98 % classification accuracy. Our two-stage clusiVAT method is applied to 26,039 trajectories of vehicles and pedestrians from a parking lot scene from the real life MIT trajectories dataset. The first stage clusters the trajectories ignoring directionality. The second stage divides the clusters obtained from the first stage by considering trajectory direction. We show that our novel two-stage clusiVAT approach can produce natural and informative trajectory clusters on this real life dataset while finding representative anomalies.
Language eng
DOI 10.1007/s00371-015-1192-x
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 880108 Road Public Transport
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Grant ID LP120100529
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082147

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