Anomalous window discovery for linear intersecting paths

IEEE Transactions on Knowledge and Data Engineering

Volumn 23, Issue 12, 2011, Pages 1857-1871

  Shi, Lei ^a   Janeja, Vandana P ^a  

^a UNIVERSITY OF MARYLAND BALTIMORE COUNTY   (United States)

Author keywords

anomaly detection; linear scan statistic; Spatial scan statistics; spatial scan window

Indexed keywords

ACCIDENT DATA; ANOMALY DETECTION; DATA POINTS; HYPOTHESIS TESTING; INVARIANT ALGORITHMS; LIKELIHOOD RATIOS; MONTE CARLO SIMULATION; OBSERVATION POINT; P-VALUES; PATH DISCOVERY; SCAN PROCESS; SCAN STATISTICS; SPATIAL COORDINATES; SPATIAL SCAN STATISTICS; SPATIAL SCAN WINDOW; SPEED-UPS; UPPER BOUND; WINDOW SIZE;

ACCIDENTS; ALGORITHMS; COMPUTER SIMULATION;

MONTE CARLO METHODS;

EID: 80054896084     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2010.212     Document Type: Article

References (40)

1. 2001 State Data for Fatalities Relating to Roadway, Pedestrian and Large Trucks, 2001.
2. V. Barnett and T. Lewis, Outliers in Statistical Data, third ed. John Wiley and Sons, 1994.
3. J. Besag and J. Newell, "The Detection of Clusters in Rare Diseases," J. Royal Statistical Soc., vol. 154, pp. 143-155, 1991.
4. L. Duczmal and A. Renato, "A Simulated Annealing Strategy for the Detection of Arbitrarily Shaped Spatial Clusters," Computational Statistics and Data Analysis, vol. 45, no. 2, pp. 269-286, 2004. (Pubitemid 38204903)
5. E. Keogh, J. Lin, and A. Fu, "Hot Sax: Efficiently Finding the Most Unusual Time Series Subsequence," Proc. IEEE Fifth Int'l Conf. Data Mining. 2005.
6. M. Ester, H.P. Kriegel, J. Sander, and X. Xu, "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases," Proc. Second Int'l. Conf. Knowledge Discovery and Data Mining, pp. 44-49, 1996.
7. A. Getis, "Reflections on Spatial Autocorrelation," Regional Science and Urban Economics, vol. 37, no. 4, pp. 491-496, 2007. (Pubitemid 46825216)
8. J. Glaz and N. Balakrishnan, Scan Statistics and Applications. Birkhauser, 1999.
9. J. Glaz, J. Naus, and S. Wallenstein, Scan Statistics. Springer, 2001.
10. J. Glaz and Z. Zhang, "Multiple Window Discrete Scan Statistics," J. Applied Statistics, vol. 31, pp. 967-980, 2004. (Pubitemid 39518787)
11. D. Griffith, Spatial Autocorrelation: A Primer. Assoc. of Am. Geographers, 1987.
12. V. Guralnik and J. Srivastava, "Event Detection from Time Series Data," KDD '99: Proc. Fifth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 33-42, 1999.
13. R. Haining, Spatial Data Analysis: Theory and Practice. Cambridge Univ. Press, 2003.
14. Highway Research &Technology, the Need for Greater Inverstment, 1999.
15. V.S. Iyengar, "On Detecting Space-Time Clusters," Proc. Tenth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '04), pp. 587-592, 2004. (Pubitemid 40114975)
16. V. Janeja and V. Atluri, "LS3: A Linear Semantic Scan Statistic Technique for Detecting Anomalous Windows," Proc. ACM Symp. Applied Computing, 2005.
17. V. Janeja and V. Atluri, "Random Walks to Identify Anomalous Free-Form Spatial Scan Windows," IEEE Trans. Knowledge and Data Eng., vol. 20, no. 10, pp. 1378-1392, Oct. 2008.
18. M. Kulldorff, "A Spatial Scan Statistic," Comm. of Statistics-Theory Meth., vol. 26, no. 6, pp. 1481-1496, 1997. (Pubitemid 127447921)
19. M. Kulldorff, W. Athas, E. Feuer, B. Miller, and C. Key, "Evaluating Cluster Alarms: A Space-Time Scan Statistic and Brain Cancer in Los Alamos," Am. J. Public Health, vol. 88, no. 9, pp. 1377-1380, 1998. (Pubitemid 28402278)
20. J.-G. Lee, J. Han, and X. Li, "Trajectory Outlier Detection: A Partition-and-Detect Framework," Proc. IEEE 24th Int'l Conf. Data Eng. (ICDE '08), 2008.
21. J. Lin, E. Keogh, S. Lonardi, and B. Chiu, "A Symbolic Representation of Time Series, with Implications for Streaming Algorithms," DMKD '03: Proc. Eighth ACM SIGMOD Workshop Research Issues in Data Mining and Knowledge Discovery, pp. 2-11, 2003.
22. H.J. Miller, "Tobler's First Law and Spatial Analysis," Annals of the Assoc. of Am. Geographers, vol. 94, no. 2, pp. 284-89, 2004. (Pubitemid 41716617)
23. S. Mohammadi, V. Janeja, and A. Gangopadhyay, "Discretized Spatio-Temporal Scan Window," Proc. Ninth SIAM Int'l Conf. Data Mining, 2009.
24. J. Naus, "The Distribution of the Size of the Maximum Cluster of Points on the Line," J. the Am. Statistical Assoc., vol. 60, pp. 532-538, 1965.
25. J.I. Naus and S. Wallenstein, "Multiple Window and Cluster Size Scan Procedures," Methodology and Computing in Applied Probability, vol. 6, pp. 389-400, 2004.
26. D. Neill, A. Moore, F. Pereira, and T. Mitchell, "Detecting Significant Multidimensional Spatial Clusters," Advances in Neural Information Processing Systems 17. pp. 969-976, MIT Press, 2005.
27. New Jersey Accident Data for State Routes, http://www.state.nj.us/ transportation/refdata/accident/, 1999.
28. New Jersey Safe Corridor Program, 2007.
29. Nj State Routes Straight Line Diagrams, 2009.
30. S. Openshaw, "A Mark 1 Geographical Analysis Machine for the Automated Analysis of Point Data Sets," Int'l. J. Geographical Information Science, vol. 1, no. 4, pp. 335-358, 1987.
31. R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules in Large Databases," Proc. 20th Int'l Conf. Very Large Data Bases, pp. 487-499, Sept. 1994.
32. Sslip : Code, Datasets and Known Window Reports, http:// www.umbc.edu/people/leishi1, 2009.
33. O. Sugiura, "Testing Change-Points with Linear Trend," 1994.
34. T. Tango and K. Takahashi, "A Flexibly Shaped Spatial Scan Statistic for Detecting Clusters," Int'l J. Health Geographics, vol. 4, no. 1, p. 11, 2005.
35. O. Tim, L. Firoiu, and P. Cohen, "Clustering Time Series with Hidden Markov Models and Dynamic Time Warping," Proc. Int'l Joint Conf. Artifical Intelligence (IJCAI '99) Workshop Sequence Learning, 1999.
36. W. Tobler, "A Computer Model Simulation of Urban Growth in the Detroit Region," Economic Geography, vol. 46, no. 2, pp. 234-240, 1970.
37. Njdot, Top 100 Intersection Crash Locations in Nj, 1998/1999.
38. L. Wei, E. Keogh, and X. Xi, "Saxually Explicit Images: Finding Unusual Shapes," ICDM '06: Proc. IEEE Sixth Int'l Conf. Data Mining, pp. 711-720, 2006. (Pubitemid 47485849)
39. K. Yamanishi and J. ichi Takeuchi, "A Unifying Framework for Detecting Outliers and Change Points from Non-Stationary Time Series Data," KDD '02: Proc. Eighth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 676-681, 2002.
40. D. Yankov, E. Keogh, and U. Rebbapragada, "Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets," Proc. Seventh IEEE Int'l Conf. Data Mining, 2007.