Transcript Week 3

Alla Petrakova
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Trajectory Clustering
TRACLUS
UCF Motion Pattern Algorithm
Attempt to find a Generally Accepted Quantiative Measure
QUALITATIVE
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Ground truth
Visual inspection
Synthetic datasets
Comparison to another
algorithm
QUANTITATIVE
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Correct Clustering Rate
Sum of Squared Error
Accuracy Measure
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± Error or Noise Penalty
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J. gil Lee and J. Han. Trajectory clustering: A
partition-and-group framework. In Proceedings
of the ACM International Conference on
Management of Data (SIGMOD), Beijing,
China, pages 593–604, 2007. Cited by 357
N denotes the set of all noise line segments.
B. Morris and M. Trivedi, “Learning Trajectory
Patterns by Clustering: Experimental Studies
and Comparative Evaluation,” Proc. IEEE Conf.
Computer Vision and Pattern Recognition, pp.
312- 319, June 2009.
Find one-to-one mapping between the ground truth
and clustering labels which maximized the number
of matches.
 where N is the total number of trajectories and pc
denotes the total number of trajectories matched to
the c-th cluster.
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IN – total number of clusters
bi = the number of labeled trajectories that
are most frequent in a given cluster
Bi = the total number of trajectories in a
cluster
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Dataset:
Used in Following Papers:
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M. Vlachos, G. Kollios, and D. Gunopulos, “Discovering Similar Multidimensional
Trajectories,” Proc. Int’l Conf. Data Eng., pp. 673- 684, 2002. (cited by 631)
Lei Chen, M. Tamer Özsu, and Vincent Oria. 2005. Robust and fast similarity search for
moving object trajectories. In Proc. of the 2005 ACM SIGMOD int’l conf. on Management of
data (SIGMOD '05). ACM, New York, NY, USA, 491-502. DOI=10.1145/1066157.1066213
(Cited by 395)
A. Naftel and S. Khalid, “Motion Trajectory Learning in the DFT- Coefficient Feature
Space,” Proc. IEEE Int’l Conf. Computer Vision Systems, pp. 47-47, Jan. 2006. (cited by 26)
W. Hu, X. Li, G. Tian, S. Maybank, and Z. Zhang, ” An Incremental DPMM-Based Method for
Trajectory Clustering, Modeling, and Retrieval”, IEEE Transactions on Pattern Analysis and
Machine Intelligence, VOL. 35, NO. 5, MAY 2013
Tsumoto, S., Hirano, S.: Detection of risk factors using trajectory mining. J. Intell. Inf. Syst.
36(3), 403–425 (2011) (cited by 15)
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No meaningful results
Separating out individual trajectories