REU Report II

1. REU Report II Alla Petrakova

2. Work overview • Becoming familiar with Motion Pattern algorithms described in: • Similarity Invariant Classification of Events by KL Divergence Minimization by Khokhar, Saleemi, Shah • Scene Understanding by Statistical Modeling of Motion Patterns bySaleemi, Hartung, Shah

3. List of papers • Gathering a comprehensive list of state of the art Trajectory Clustering methods used in Data Mining. • 25 articles and counting • Finding data sets used • Finding code – if available • Testing against motion pattern algorithm

4. TRACLUS, MoveMine • Clustering and data mining reading: • Trajectory Clustering: A partition-and-group frameworkby Lee, Hanand Whang

5. Trajectory Clustering: Partition-and-Group Framework TRACLUS and MoveMine

6. TRACLUS Written by Lee, Han, Whang in 1997 Serves as foundation for MoveMineset of works 357 citations

7. TRACLUS Preciseness vs Conciseness Characteristic points – points where the behavior of trajectory changes rapidly MDL (Minimum Description Length) principle L(H) conciseness (hypothesis) L(D|H) preciseness

8. TRACLUS Distance formula:

9. dist(Li,Lj) = w⊥ ·d⊥(Li,Lj)+w∥ ·d∥(Li,Lj)+ wθ ·dθ(Li,Lj) • The optimal partitioning of a trajectory should possess two desirable properties: preciseness and conciseness. Pre- ciseness means that the difference between a trajectory and a set of its trajectory partitions should be as small as possible. • Weights may differ depending on application. We will use w = 1 for all of them. • From “Noisy Logo Recognition Using Line Segment Hausdorff Distance” paper • Modified Line Hausdorff Distance

10. TRACLUS • MDL cost = L(H) + L(D|H) • L(H) represents the sum of the length of all trajectory partitions(conciseness) • L(D|H) represents the number of segments that deviate from actual trajectory (preciseness) • We need to find the optimal partitioning that minimizes L(H ) + L(D|H ). This is exactly the tradeoff between preciseness and conciseness.

11. TRACLUS • Clustering: • Based on DBSCAN • Parameters common to TRUCLUS and DBSCAN • ε – the maximum distance • MinLns – minimum number of line segments in a cluster • Parameter unique to TRUCLUS: • Trajectory cardinality of a cluster: PTR(Ci) = {TR(Lj) | ∀Lj ∈ Ci}

12. TRUCLUS • Parameter selection • ε - simulated annealing • MinLns –average number of lines at an optimal ε • Complexity – • O(n2) • Depending on organization and indexing of data (line segments), complexity can be reduced to O(n long n)

13. TRACLUS versus Motion Pattern Testing against motion pattern algorithm

14. Oregon Wildlife – Elk1993 • Elk 1993: • 33 trajectories • 47,204 points Used in the following papers: • 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 • ElioMasciari. 2012. Finding homogeneous groups in trajectory streams. In Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '12). ACM, New York, NY, USA, 11-18. DOI=10.1145/2442968.2442970 http://doi.acm.org/10.1145/2442968.2442970 • Zhenhui Li, Jae-Gil Lee, Xiaolei Li, and Jiawei Han. 2010. Incremental clustering for trajectories. In Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II (DASFAA'10), Hiroyuki Kitagawa, Yoshiharu Ishikawa, Qing Li, and Chiemi Watanabe (Eds.), Vol. Part II. Springer-Verlag, Berlin, Heidelberg, 32-46. DOI=10.1007/978-3-642-12098-5_3 http://dx.doi.org/10.1007/978-3-642-12098-5_3 • ElioMasciari. 2009. A Complete Framework for Clustering Trajectories. In Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI '09). IEEE Computer Society, Washington, DC, USA, 9-16. DOI=10.1109/ICTAI.2009.31 http://dx.doi.org/10.1109/ICTAI.2009.31 • Yu Zhang and Dechang Pi. 2009. A Trajectory Clustering Algorithm Based on Symmetric Neighborhood. In Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 03 (CSIE '09), Vol. 3. IEEE Computer Society, Washington, DC, USA, 640-645. DOI=10.1109/CSIE.2009.366 http://dx.doi.org/10.1109/CSIE.2009.366 • Jae-Gil Lee, Jiawei Han, Xiaolei Li, and Hector Gonzalez. 2008. TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. Proc. VLDB Endow. 1, 1 (August 2008), 1081-1094. • Jae-Gil Lee, Jiawei Han, and Xiaolei Li. 2008. Trajectory Outlier Detection: A Partition-and-Detect Framework. In Proceedings of the 2008 IEEE 24th International Conference on Data Engineering (ICDE '08). IEEE Computer Society, Washington, DC, USA, 140-149. DOI=10.1109/ICDE.2008.4497422 http://dx.doi.org/10.1109/ICDE.2008.4497422

15. Elk1993 TRACLUS UCF

16. Oregon Wildlife – Deer1995 • Deer1995 • 32 trajectories • 20,065 data points Used in the following papers: • 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 • ElioMasciari. 2012. Finding homogeneous groups in trajectory streams. In Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS '12). ACM, New York, NY, USA, 11-18. DOI=10.1145/2442968.2442970 http://doi.acm.org/10.1145/2442968.2442970 • Zhenhui Li, Jae-Gil Lee, Xiaolei Li, and Jiawei Han. 2010. Incremental clustering for trajectories. In Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II (DASFAA'10), Hiroyuki Kitagawa, Yoshiharu Ishikawa, Qing Li, and Chiemi Watanabe (Eds.), Vol. Part II. Springer-Verlag, Berlin, Heidelberg, 32-46. DOI=10.1007/978-3-642-12098-5_3 http://dx.doi.org/10.1007/978-3-642-12098-5_3 • ElioMasciari. 2009. A Complete Framework for Clustering Trajectories. In Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI '09). IEEE Computer Society, Washington, DC, USA, 9-16. DOI=10.1109/ICTAI.2009.31 http://dx.doi.org/10.1109/ICTAI.2009.31 • Yu Zhang and Dechang Pi. 2009. A Trajectory Clustering Algorithm Based on Symmetric Neighborhood. In Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 03 (CSIE '09), Vol. 3. IEEE Computer Society, Washington, DC, USA, 640-645. DOI=10.1109/CSIE.2009.366 http://dx.doi.org/10.1109/CSIE.2009.366 • Jae-Gil Lee, Jiawei Han, Xiaolei Li, and Hector Gonzalez. 2008. TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. Proc. VLDB Endow. 1, 1 (August 2008), 1081-1094. • Jae-Gil Lee, Jiawei Han, and Xiaolei Li. 2008. Trajectory Outlier Detection: A Partition-and-Detect Framework. In Proceedings of the 2008 IEEE 24th International Conference on Data Engineering (ICDE '08). IEEE Computer Society, Washington, DC, USA, 140-149. DOI=10.1109/ICDE.2008.4497422 http://dx.doi.org/10.1109/ICDE.2008.4497422

17. Deer1995 TRACLUS UCF

18. Swainson’s Hawks (Movebank) • Swainson’s Hawks • 43 trajectories • 4514 points • Follows migration route • Closest we have to ground truth “Swainson'sHawks converged in eastern Mexico on the Gulf of Mexico coast. Southward, these hawks followed a narrow, well-defined path through Central America, across the Andes Mountains in Columbia, and east of the Andes to central Argentina where they all spent the austral summer. Swainson's Hawks northward migration largely retraced their southward route.” Fuller, M.R., Seegar, W.S., Schueck, L.S., 1998. Routes and Travel Rates of Migrating Peregrine Falcons Falco peregrinus and Swainson's Hawks Buteoswainsoni in the Western Hemisphere. Journal of Avian Biology 29:433-440.

19. Swainson’s Hawks TRACLUS UCF

20. Buffalo dataset