Outlier Detection for Temporal Data: A Survey

IEEE Transactions on Knowledge and Data Engineering

Volumn 26, Issue 9, 2014, Pages 2250-2267

  Gupta, Manish ^a   Gao, Jing ^b   Aggarwal, Charu C ^c   Han, Jiawei ^d  

^a MICROSOFT   (United States)

^b UNIVERSITY AT BUFFALO   (United States)

^c IBM T J WATSON RESEARCH CENTER   (United States)

^d UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Data mining; Mining methods and algorithms

Indexed keywords

COMPUTER HARDWARE; DATA HANDLING; HARDWARE; INFORMATION MANAGEMENT; RECONFIGURABLE HARDWARE; SOFTWARE ENGINEERING; STATISTICS; SURVEYS; TIME SERIES;

APPLICATION SCENARIO; HARDWARE AND SOFTWARE; HARDWARE TECHNOLOGY; MANAGEMENT MECHANISMS; MINING METHODS AND ALGORITHMS; OUTLIER DETECTION; SOFTWARE TECHNOLOGY; SPATIO-TEMPORAL DATA;

DATA MINING;

EID: 84959505571     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2013.184     Document Type: Review

References (162)

1. C. C. Aggarwal, Outlier Analysis. New York, NY, USA: Springer, 2013.
2. V. Chandola, A. Banerjee, and V. Kumar, "Anomaly detection: A survey," ACM Comput. Surv., vol. 41, no. 3, pp. 15:1-15:58, 2009.
3. V. J. Hodge and J. Austin, "A survey of outlier detection methodologies," Artif. Intell. Rev., vol. 22, no. 2, pp. 85-126, 2004.
4. Y. Zhang, N. Meratnia, and P. J. M. Havinga, "Outlier detection techniques for wireless sensor networks: A survey," Centre Telemat. Inform. Technol. Univ. Twente, Enschede, The Netherlands, Tech. Rep. TR-CTIT-08-59, Oct. 2008.
5. C. C. Aggarwal and P. S. Yu, "Outlier detection for high dimensional data," SIGMOD Rec., vol. 30, pp. 37-46, May 2001.
6. C. C. Aggarwal and P. S. Yu, "Outlier detection with uncertain data," in Proc. 2008 SIAM Int. Conf. SDM, pp. 483-493.
7. C. Aggarwal and K. Subbian, "Event detection in social streams," in Proc. 12th SIAM Int. Conf. SDM, 2012, pp. 624-635.
8. C. C. Aggarwal, "On abnormality detection in spuriously populated data streams," in Proc. 2005 SIAM Int. Conf. SDM, pp. 80-91.
9. C. C. Aggarwal, Y. Zhao, and P. S. Yu, "Outlier detection in graph streams," in Proc. 27th ICDE, Hannover, Germany, 2011, pp. 399-409.
10. J. Gao et al., "On community outliers and their efficient detection in information networks," in Proc. 16th ACM Int. Conf. KDD, 2010, pp. 813-822.
11. A. Ghoting, M. E. Otey, and S. Parthasarathy, "LOADED: Link-based outlier and anomaly detection in evolving data sets," in Proc. 4th IEEE ICDM, 2004, pp. 387-390.
12. M. Gupta, J. Gao, Y. Sun, and J. Han, "Community trend outlier detection using soft temporal pattern mining," in Proc. ECML PKDD, Bristol, U.K., 2012, pp. 692-708.
13. M. Gupta, J. Gao, Y. Sun, and J. Han, "Integrating community matching and outlier detection for mining evolutionary community outliers," in Proc. 18th ACM Int. Conf. KDD, Beijing, China, 2012, pp. 859-867.
14. J. P. Burman and M. C. Otto, "Census bureau research project: Outliers in time series," Bureau of the Census, SRD Res. Rep. CENSUS/SRD/RR-88114, May 1988.
15. A. J. Fox, "Outliers in time series," J. Roy. Statist. Soc. B Methodol., vol. 34, no. 3, pp. 350-363, 1972.
16. H. Cho, Y. jin Kim, H. J. Jung, S.-W. Lee, and J. W. Lee, "OutlierD: An R package for outlier detection using quantile regression on mass spectrometry data," Bioinformatics, vol. 24, no. 6, pp. 882-884, 2008.
17. V. Barnett and T. Lewis, Outliers in Statistical Data. New York, NY, USA: Wiley, 1978.
18. D. M. Hawkins, Identification of Outliers. London, U.K.: Chapman and Hall, 1980.
19. P. J. Rousseeuw and A. M. Leroy, Robust Regression and Outlier Detection. New York, NY, USA: Wiley, 1987.
20. T. Lane et al., "Sequence matching and learning in anomaly detection for computer security," in Proc. AAAI Workshop AI Approaches Fraud Detection Risk Manage., 1997, pp. 43-49.
21. S. Budalakoti, A. Srivastava, R. Akella, and E. Turkov, "Anomaly detection in large sets of high-dimensional symbol sequences," NASA Ames Res. Center, Mountain View, CA, USA, Tech. Rep. NASA TM-2006-214553, 2006.
22. S. Budalakoti, A. N. Srivastava, and M. E. Otey, "Anomaly detection and diagnosis algorithms for discrete symbol sequences with applications to airline safety," IEEE Trans. Syst., Man, Cybern., C, Appl. Rev., vol. 39, no. 1, pp. 101-113, Jan. 2009.
23. V. Chandola, V. Mithal, and V. Kumar, "A comparative evaluation of anomaly detection techniques for sequence data," in Proc. 2008 8th IEEE ICDM, Pisa, Italy, pp. 743-748.
24. K. Sequeira and M. Zaki, "ADMIT: Anomaly-based data mining for intrusions," in Proc. 8th ACM Int. Conf. KDD, New York, NY, USA, 2002, pp. 386-395.
25. A. Nairac et al., "A system for the analysis of jet engine vibration data," Integr. Comput. Aided Eng., vol. 6, no. 1, pp. 53-66, Jan. 1999.
26. X. Pan, J. Tan, S. Kavulya, R. Gandhi, and P. Narasimhan, "Ganesha: BlackBox diagnosis of mapReduce systems," SIGMETRICS Perform. Eval. Rev., vol. 37, no. 3, pp. 8-13, Jan. 2010.
27. U. Rebbapragada, P. Protopapas, C. E. Brodley, and C. Alcock, "Finding anomalous periodic time series," J. Mach. Learn., vol. 74, no. 3, pp. 281-313, Mar. 2009.
28. L. Portnoy, E. Eskin, and S. Stolfo, "Intrusion detection with unlabeled data using clustering," in Proc. ACM CSS Workshop DMSA, 2001, pp. 5-8.
29. M. Gupta, A. B. Sharma, H. Chen, and G. Jiang, "Context-aware time series anomaly detection for complex systems," in Proc. SDM Workshop, 2013.
30. E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo, "A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data," in Proc. Appl. Data Mining Comput. Security, 2002.
31. P. Evangelista, P. Bonnisone, M. Embrechts, and B. Szymanski, "Fuzzy ROC curves for the 1 class SVM: Application to intrusion detection," in Proc. 13th Eur. Symp. Artif. Neural Netw., 2005, pp. 345-350.
32. J. Ma and S. Perkins, "Time-series novelty detection using one-class support vector machines," in Proc. IJCNN, Jul. 2003, pp. 1741-1745.
33. B. Szymanski and Y. Zhang, "Recursive data mining for masquerade detection and author identification," in Proc. 5th Annu. IEEE SMC Inform. Assur. Workshop, 2004, pp. 424-431.
34. F. A. González and D. Dasgupta, "Anomaly detection using real-valued negative selection," Genet. Program. Evolvable Mach., vol. 4, no. 4, pp. 383-403, Dec. 2003.
35. C. Marceau, "Characterizing the behavior of a program using multiple-length n-grams," in Proc. 2000 NSPW, pp. 101-110.
36. C. C. Michael and A. Ghosh, "Two state-based approaches to program-based anomaly detection," in Proc. 16th ACSAC, New Orleans, LA, USA, 2000, pp. 21-30.
37. S. Salvador and P. Chan, "Learning states and rules for detecting anomalies in time series," Appl. Intell., vol. 23, no. 3, pp. 241-255, Dec. 2005.
38. N. Ye, "A Markov chain model of temporal behavior for anomaly detection," in Proc. 2000 IEEE SMC Inform. Assur. Security Workshop, vol. 166. pp. 171-174.
39. J. Yang and W. Wang, "CLUSEQ: Efficient and effective sequence clustering," in Proc. 19th ICDE, 2003, pp. 101-112.
40. P. Sun, S. Chawla, and B. Arunasalam, "Mining for outliers in sequential databases," in Proc. 6th SIAM Int. Conf. SDM, 2006, pp. 94-105.
41. E. Eskin, W. Lee, and S. Stolfo, "Modeling system calls for intrusion detection with dynamic window sizes," in Proc. DISCEX, vol. 1. 2001, pp. 165-175.
42. W. Lee, S. J. Stolfo, and P. K. Chan, "Learning patterns from unix process execution traces for intrusion detection," in Proc. AAAI Workshop AI Approaches Fraud Detection Risk Manage., 1997, pp. 50-56.
43. G. Florez-Larrahondo, S. M. Bridges, and R. Vaughn, "Efficient modeling of discrete events for anomaly detection using hidden Markov models," in Proc. 8th Int. Conf. ISC, 2005, pp. 506-514.
44. B. Gao, H.-Y. Ma, and Y.-H. Yang, "HMMs (Hidden Markov Models) based on anomaly intrusion detection method," in Proc. Int. Conf. Mach. Learn. Cybern., 2002, pp. 381-385.
45. Y. Qiao, X. Xin, Y. Bin, and S. Ge, "Anomaly intrusion detection method based on HMM," Electron. Lett., vol. 38, no. 13, pp. 663-664, Jun. 2002.
46. X. Zhang, P. Fan, and Z. Zhu, "A new anomaly detection method based on hierarchical HMM," in Proc. 4th Int. Conf. PDCAT, Aug. 2003, pp. 249-252.
47. X. Li and J. Han, "Mining approximate top-k subspace anomalies in multi-dimensional time-series data," in Proc. 33rd Int. Conf. VLDB, Vienna, Austria, 2007, pp. 447-458.
48. W. Lee and S. J. Stolfo, "Data mining approaches for intrusion detection," in Proc. 7th Conf. USENIX SSYM, Berkeley, CA, USA, 1998, pp. 6-20.
49. J. B. D. Cabrera, L. Lewis, and R. K. Mehra, "Detection and classification of intrusions and faults using sequences of system calls," SIGMOD Rec., vol. 30, no. 4, pp. 25-34, Dec. 2001.
50. D. Dasgupta and F. Nino, "A comparison of negative and positive selection algorithms in novel pattern detection," in Proc. IEEE Int. Conf. SMC, Nashville, TN, USA, 2000, pp. 125-130.
51. D. Endler, "Intrusion detection applying machine learning to solaris audit data," in Proc. 14th ACSAC, Phoenix, AZ, USA, 1998, pp. 268-279.
52. A. K. Gosh, J. Wanken, and F. Charron, "Detecting anomalous and unknown intrusions against programs," in Proc. 14th ACSAC, Phoenix, AZ, USA, 1998, pp. 259-267.
53. A. Ghosh, A. Schwartzbard, and M. Schatz, "Learning program behavior profiles for intrusion detection," in Proc. 1st USENIX Workshop Intrusion Detection Network Monitoring, Berkeley, CA, USA, 1999, pp. 51-62.
54. A. K. Ghosh and A. Schwartzbard, "A study in using neural networks for anomaly and misuse detection," in Proc. 8th Conf. USENIX SSYM, Berkeley, CA, USA, 1999, pp. 12-23.
55. X. Li, J. Han, and S. Kim, "Motion-alert: Automatic anomaly detection in massive moving objects," in Proc. 4th IEEE Int. Conf. ISI, San Diego, CA, USA, 2006, pp. 166-177.
56. D.-K. Kang, D. Fuller, and V. Honavar, "Learning classifiers for misuse detection using a bag of system calls representation," in Proc. 3rd IEEE Int. Conf. ISI, Atlanta, GA, USA, 2005, pp. 511-516.
57. S. Tian, S. Mu, and C. Yin, "Sequence-similarity kernels for SVMs to detect anomalies in system calls," Neurocomputing, vol. 70, no. 4-6, pp. 859-866, Jan. 2007.
58. M. Wang, C. Zhang, and J. Yu, "Native API based windows anomaly intrusion detection method using SVM," in Proc. IEEE Int. Conf. SUTC, vol 1. 2006, pp. 514-519.
59. X. Li, J. Han, S. Kim, and H. Gonzalez, "ROAM: Rule- and motif-based anomaly detection in massive moving object data sets," in Proc. 7th SIAM Int. Conf. SDM, 2007, pp. 273-284.
60. A. Ghosh et al., "Using program behavior profiles for intrusion detection," in Proc. SANS Intrusion Detection Workshop, 1999.
61. S. A. Hofmeyr, S. Forrest, and A. Somayaji, "Intrusion detection using sequences of system calls," J. Comput. Security, vol. 6, no. 3, pp. 151-180, Aug. 1998.
62. T. Lane and C. E. Brodley, "An application of machine learning to anomaly detection," in Proc. 20th NISSC, 1997, pp. 366-380.
63. T. Lane and C. E. Brodley, "Temporal sequence learning and data reduction for anomaly detection," in Proc. 5th ACM Conf. CCS, 1998, pp. 150-158.
64. S. Forrest, S. A. Hofmeyr, A. Somayaji, and T. A. Longstaff, "A sense of self for unix processes," in Proc. IEEE Symp. Security Privacy, Oakland, CA, USA, 1996, pp. 120-128.
65. D. Dasgupta and N. Majumdar, "Anomaly detection in multidimensional data using negative selection algorithm," in Proc. Congr. CEC, Honolulu, HI, USA, 2002, pp. 1039-1044.
66. P. D'haeseleer, S. Forrest, and P. Helman, "An immunological approach to change detection: Algorithms, analysis and implications," in Proc. IEEE Symp. Security Privacy, Oakland, CA, USA, May 1996, pp. 110-119.
67. S. Forrest, A. S. Perelson, L. Allen, and R. Cherukuri, "Self-nonself discrimination in a computer," in Proc. IEEE Symp. Security Privacy, Oakland, CA, USA, 1994, pp. 202-212.
68. E. Keogh, S. Lonardi, and B. Y.-C. Chiu, "Finding surprising patterns in a time series database in linear time and space," in Proc. 8th ACM Int. Conf. KDD, Edmonton, AB, Canada, 2002, pp. 550-556.
69. M. Atallah, R. Gwadera, and W. Szpankowski, "Detection of significant sets of episodes in event sequences," in Proc. 4th IEEE ICDM, 2004, pp. 3-10.
70. J. Lin, E. Keogh, S. Lonardi, and B. Chiu, "A symbolic representation of time series, with implications for streaming algorithms," in Proc. 8th ACM SIGMOD Workshop Research Issues DMKD, San Diego, CA, USA, 2003, pp. 2-11.
71. J. Lin, E. Keogh, L. Wei, and S. Lonardi, "Experiencing SAX: A novel symbolic representation of time series," Data Mining Knowl. Discov., vol. 15, no. 2, pp. 107-144, Oct. 2007.
72. R. Gwadera, M. J. Atallah, and W. Szpankowski, "Markov models for identification of significant episodes," in Proc. 5th SIAM Int. Conf. SDM, 2005, pp. 404-414.
73. R. Gwadera, M. J. Atallah, and W. Szpankowski, "Reliable detection of episodes in event sequences," Knowl. Inform. Syst., vol. 7, no. 4, pp. 415-437, May 2005.
74. S. Basu and M. Meckesheimer, "Automatic outlier detection for time series: An application to sensor data," Knowl. Inform. Syst., vol. 11, no. 2, pp. 137-154, Feb. 2007.
75. D. J. Hill and B. S. Minsker, "Anomaly detection in streaming environmental sensor data: A data-driven modeling approach," Environ. Modell. Softw., vol. 25, no. 9, pp. 1014-1022, Sep. 2010.
76. J. Ma and S. Perkins, "Online novelty detection on temporal sequences," in Proc. 9th ACM Int. Conf KDD, New York, NY, USA, 2003, pp. 613-618.
77. N. D. Le, R. D. Martin, and A. E. Raftery, "Modeling flat stretches, bursts, and outliers in time series using mixture transition distribution models," J. Amer. Statist. Assoc., vol. 91, no. 436, pp. 1504-1515, 1996.
78. R. S. Tsay, D. Pena, and A. E. Pankratz, "Outliers in multivariate time series," Biometrika, vol. 87, no. 4, pp. 789-804, 2000.
79. A. Luceno, "Detecting possibly non-consecutive outliers in industrial time series," J. Roy. Statist. Soc. B, Statist. Methodol., vol. 60, no. 2, pp. 295-310, 1998.
80. A. Justel, D. Pena, and R. S. Tsay, "Detection of outlier patches in autoregressive time series," Statist. Sinica, vol. 11, no. 3, pp. 651-674, 2001.
81. A. M. Bianco, M. García Ben, E. J. Martínez, and V. J. Yohai, "Outlier detection in regression models with ARIMA errors using robust estimates," J. Forecasting, vol. 20, no. 8, pp. 565-579, Dec. 2001.
82. I. Chang, G. C. Tiao, and C. Chen, "Estimation of time series parameters in the presence of outliers," Technometrics, vol. 30, no. 2, pp. 193-204, 1988.
83. C. Chen and L.-M. Liu, "Joint estimation of model parameters and outlier effects in time series," J. Amer. Statist. Assoc., vol. 88, no. 421, pp. 284-297, 1993.
84. R. S. Tsay, "Time series model specification in the presence of outliers," J. Amer. Statist. Assoc., vol. 81, no. 393, pp. 132-141, 1986.
85. P. Galeano, D. Pena, and R. S. Tsay, "Outlier detection in multivariate time series by projection pursuit," J. Amer. Statist. Assoc., vol. 101, no. 474, pp. 654-669, 2006.
86. A. W. Williams, S. M. Pertet, and P. Narasimhan, "Tiresias: Black-box failure prediction in distributed systems," in Proc. 21st IPDPS, Long Beach, CA, USA, 2007, pp. 1-8.
87. G. Silvestri, F. Verona, M. Innocenti, and M. Napolitano, "Fault detection using neural networks," in Proc. IEEE Int. Conf. Neural Netw., Orlando, FL, USA, Jun. 1994, pp. 3796-3799.
88. H. V. Jagadish, N. Koudas, and S. Muthukrishnan, "Mining deviants in a time series database," in Proc. 25th Int. Conf. VLDB, Edinburgh, U.K., 1999, pp. 102-113.
89. S. Muthukrishnan, R. Shah, and J. Vitter, "Mining deviants in time series data streams," in Proc. 16th Int. Conf. SSDBM, Jun. 2004, pp. 41-50.
90. E. Keogh, J. Lin, S.-H. Lee, and H. Van Herle, "Finding the most unusual time series subsequence: Algorithms and applications," Knowl. Inform. Syst., vol. 11, no. 1, pp. 1-27, Dec. 2006.
91. E. Keogh, J. Lin, and A. Fu, "HOT SAX: Efficiently finding the most unusual time series subsequence," in Proc. 5th IEEE ICDM, 2005, pp. 226-233.
92. L. Wei, E. Keogh, and X. Xi, "SAXually explicit images: Finding unusual shapes," in Proc. 6th ICDM, Hong Kong, 2006, pp. 711-720.
93. Y. Bu et al., "WAT: Finding top-k discords in time series database," in Proc. 7th SIAM Int. Conf. SDM, 2007, pp. 449-454.
94. A. W.-C. Fu, O. T.-W. Leung, E. Keogh, and J. Lin, "Finding time series discords based on haar transform," in Proc. 2nd Int. Conf. ADMA, Xi'an, China, 2006, pp. 31-41.
95. J. Lin, E. Keogh, A. Fu, and H. Van Herle, "Approximations to magic: Finding unusual medical time series," in Proc. 18th IEEE Symp. CBMS, 2005, pp. 329-334.
96. E. Keogh, S. Lonardi, and C. A. Ratanamahatana, "Towards parameter-free data mining," in Proc. 10th ACM Int. Conf. KDD, Seattle, WA, USA, 2004, pp. 206-215.
97. D. Yankov, E. Keogh, and U. Rebbapragada, "Disk aware discord discovery: Finding unusual time series in terabyte sized datasets," Knowl. Inform. Syst., vol. 17, no. 2, pp. 241-262, Nov. 2008.
98. X.-Y. Chen and Y.-Y. Zhan, "Multi-scale anomaly detection algorithm based on infrequent pattern of time series," J. Comput. Appl. Math., vol. 214, no. 1, pp. 227-237, Apr. 2008.
99. C. Shahabi, X. Tian, and W. Zhao, "TSA-tree: A wavelet-based approach to improve the efficiency of multi-level surprise and trend queries on time-series data," in Proc. 12th Int. Conf. SSDBM, Berlin, Germany, 2000, pp. 55-68.
100. L. Wei et al., "Assumption-free anomaly detection in time series," in Proc. 17th Int. Conf. SSDBM, Berkeley, CA, USA, 2005, pp. 237-240.
101. Y. Zhu and D. Shasha, "Efficient elastic burst detection in data streams," in Proc. 9th ACM Int. Conf KDD, New York, NY, USA, 2003, pp. 336-345.
102. K. Yamanishi and J.-I. Takeuchi, "A unifying framework for detecting outliers and change points from non-stationary time series data," in Proc. 8th ACM Int. Conf. KDD, Edmonton, AB, Canada, 2002, pp. 676-681.
103. K. Yamanishi, J.-I. Takeuchi, G. Williams, and P. Milne, "On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms," Data Mining Knowl. Discov., vol. 8, no. 3, pp. 275-300, May 2004.
104. A. V. Harold and S. Javitz, "The NIDES statistical component description and justification," SRI Int., Menlo Park, CA, USA, Tech. Rep. A010, Mar. 1994.
105. C. C. Aggarwal and P. S. Yu, "On clustering massive text and categorical data streams," Knowl. Inform. Syst., vol. 24, no. 2, pp. 171-196, 2010.
106. D. J. Hill, B. S. Minsker, and E. Amir, "Real-time Bayesian anomaly detection for environmental sensor data," in Proc. 32nd Conf. IAHR, 2007.
107. E. M. Knorr and R. T. Ng, "Algorithms for mining distance-based outliers in large datasets," in Proc. 24th Int. Conf. VLDB, New York, NY, USA, 1998, pp. 392-403.
108. F. Angiulli and F. Fassetti, "Detecting distance-based outliers in streams of data," in Proc. 16th ACM CIKM, Lisboa, Portugal, 2007, pp. 811-820.
109. D. Yang, E. A. Rundensteiner, and M. O. Ward, "Neighbor-based pattern detection for windows over streaming data," in Proc. 12th Int. Conf. EDBT, 2009, pp. 529-540.
110. Y. Bu, L. Chen, A. W.-C. Fu, and D. Liu, "Efficient anomaly monitoring over moving object trajectory streams," in Proc. 15th ACM Int. Conf. KDD, Paris, France, 2009, pp. 159-168.
111. H. Cao, Y. Zhou, L. Shou, and G. Chen, "Attribute outlier detection over data streams," in Proc. 15th Int. Conf. DASFAA Part II, Tsukuba, Japan, 2010, pp. 216-230.
112. M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, "LOF: Identifying density-based local outliers," in Proc. ACM SIGMOD Int. Conf. SIGMOD, New York, NY, USA, 2000, pp. 93-104.
113. D. Pokrajac, A. Lazarevic, and L. J. Latecki, "Incremental local outlier detection for data streams," in Proc. IEEE Symp. CIDM, Honolulu, HI, USA, Apr. 2007, pp. 504-515.
114. J. Zhang, Q. Gao, and H. Wang, "SPOT: A system for detecting projected outliers from high-dimensional data streams," in Proc. 24th ICDE, Cancun, Mexico, 2008, pp. 1628-1631.
115. J. Branch, B. Szymanski, C. Giannella, R. Wolff, and H. Kargupta, "In-network outlier detection in wireless sensor networks," in Proc. 26th IEEE ICDCS, 2006, pp. 51-81.
116. M. E. Otey, A. Ghoting, and S. Parthasarathy, "Fast distributed outlier detection in mixed-attribute data sets," Data Mining Knowl. Discov., vol. 12, nos. 2-3, pp. 203-228, May 2006.
117. S. Subramaniam, T. Palpanas, D. Papadopoulos, V. Kalogeraki, and D. Gunopulos, "Online outlier detection in sensor data using non-parametric models," in Proc. 32nd Int. Conf. VLDB, Seoul, Korea, 2006, pp. 187-198.
118. T. Palpanas, D. Papadopoulos, V. Kalogeraki, and D. Gunopulos, "Distributed deviation detection in sensor networks," SIGMOD Rec., vol. 32, no. 4, pp. 77-82, Dec. 2003.
119. N. A. C. Cressie, Statistics for Spatial Data. New York, NY, USA: Wiley, 1993.
120. M. Jun, H. Jeong, and C. Kuo, "Distributed spatio-temporal outlier detection in sensor networks," in Proc. SPIE, 2005, pp. 760-763.
121. C. Franke and M. Gertz, "Detection and exploration of outlier regions in sensor data streams," in Proc. IEEE ICDMW, Pisa, Italy, 2008, pp. 375-384.
122. C. Franke and M. Gertz, "ORDEN: Outlier region detection and exploration in sensor networks," in Proc. ACM SIGMOD, Providence, RI, USA, 2009, pp. 1075-1078.
123. D. Birant and A. Kut, "Spatio-temporal outlier detection in large databases," J. Comput. Inform. Technol., vol. 14, no. 4, pp. 291-297, 2006.
124. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," in Proc. 2nd ACM Int. Conf. KDD, 1996, pp. 226-231.
125. T. Cheng and Z. Li, "A hybrid approach to detect spatial-temporal outliers," in Proc. 12th Int. Conf. Geoinformatics, 2004, pp. 173-178.
126. T. Cheng and Z. Li, "A multiscale approach for spatio-temporal outlier detection," Trans. GIS, vol. 10, no. 2, pp. 253-263, 2006.
127. N. R. Adam, V. P. Janeja, and V. Atluri, "Neighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets," in Proc. ACM SAC, Nicosia, Cyprus, 2004, pp. 576-583.
128. E. Wu, W. Liu, and S. Chawla, "Spatio-temporal outlier detection in precipitation data," in Proc. 2nd Int. Conf. Sensor KDD, Las Vegas, NV, USA, 2010, pp. 115-133.
129. C.-T. Lu and L. R. Liang, "Wavelet fuzzy classification for detecting and tracking region outliers in meteorological data," in Proc. 12th Annu. ACM Int. Workshop GIS, 2004, pp. 258-265.
130. J.-G. Lee, J. Han, and X. Li, "Trajectory outlier detection: A partition-and-detect framework," in Proc. IEEE 24th ICDE, Washington, DC, USA, 2008, pp. 140-149.
131. Y. Ge et al., "Top-eye: Top-k evolving trajectory outlier detection," in Proc. 19th ACM Int. CIKM, Toronto, ON, Canada, 2010, pp. 1733-1736.
132. X. Li, Z. Li, J. Han, and J.-G. Lee, "Temporal outlier detection in vehicle traffic data," in Proc. 2009 IEEE ICDE, Shanghai, China, pp. 1319-1322.
133. B. Pincombe, "Anomaly detection in time series of graphs using ARMA processes," ASOR Bull., vol. 24, no. 4, pp. 2-10, 2005.
134. P. Papadimitriou, A. Dasdan, and H. Garcia-Molina, "Web graph similarity for anomaly detection," in Proc. 17th Int. Conf. WWW, 2008, pp. 1167-1168.
135. P. Shoubridge, M. Kraetzl, and D. Ray, "Detection of abnormal change in dynamic networks," in Proc. Int. Conf. IDC, Adelaide, SA, Australia, 1999, pp. 557-562.
136. K. M. Kapsabelis, P. J. Dickinson, and K. Dogancay, "Investigation of graph edit distance cost functions for detection of network anomalies," in Proc. 13th Biennial CTAC, vol. 48. Oct. 2007, pp. C436-C449.
137. P. Dickinson, H. Bunke, A. Dadej, and M. Kraetzl, "Median graphs and anomalous change detection in communication networks," in Proc. Int. Conf. Inform., Decis. Control, Adelaide, SA, Australia, Feb. 2002, pp. 59-64.
138. M. Gaston, M. Kraetzl, and W. Wallis, "Graph diameter as a pseudo-metric for change detection in dynamic networks," Austr. J. Combinatorics, vol. 35, pp. 299-312, Jun. 2006.
139. P. Dickinson and M. Kraetzl, "Novel approaches in modelling dynamics of networked surveillance environment," in Proc. 6th Int. Conf. Inform. Fusion, vol. 1. Cairns, QLD, Australia, 2003, pp. 302-309.
140. P. Papadimitriou, A. Dasdan, and H. Garcia-Molina, "Web graph similarity for anomaly detection," J. Internet Serv. Appl., vol. 1, no. 1, pp. 19-30, 2010.
141. M. Gupta, C. C. Aggarwal, and J. Han, "Finding top-k shortest path distance changes in an evolutionary network," in Proc. 12th Int. Conf. Adv. SSTD, Minneapolis, MN, USA, 2011, pp. 130-148.
142. T. Idé and H. Kashima, "Eigenspace-based anomaly detection in computer systems," in Proc. 10th ACM Int. Conf. KDD, Seattle, WA, USA, 2004, pp. 440-449.
143. L. Akoglu and C. Faloutsos, "Event detection in time series of mobile communication graphs," in Proc. Army Science Conf., 2010.
144. M. Gupta, C. C. Aggarwal, J. Han, and Y. Sun, "Evolutionary clustering and analysis of bibliographic networks," in Proc. 2011 Int. Conf. ASONAM, pp. 63-70.
145. M. Gupta, J. Gao, and J. Han, "Community distribution outlier detection in heterogeneous information networks," in Proc. 2013 ECML PKDD, Prague, Czech Republic, pp. 557-573.
146. Y. Sun et al., "Detecting spatio-temporal outliers in climate dataset: A method study," in Proc. 2005 IEEE IGARSS, pp. 760-763.
147. W. Drosdowsky, "An analysis of Australian seasonal rainfall anomalies: 1950-1987," Int. J. Climatol., vol. 13, no. 1, pp. 1-30, 1993.
148. R. Lasaponara, "On the use of principal component analysis (PCA) for evaluating interannual vegetation anomalies from SPOT/VEGETATION NDVI temporal series," Ecol. Modell., vol. 194, no. 4, pp. 429-434, 2006.
149. D. Dasgupta and S. Forrest, "Novelty detection in time series data using ideas from immunology," in Proc. 5th Int. Conf. Intelligent Systems, Reno, NV, USA, 1996.
150. V. Jakkula and D. J. Cook, "Anomaly detection using temporal data mining in a smart home environment," Methods Inform. Med., vol. 47, no. 1, pp. 70-75, 2008.
151. C. Warrender, S. Forrest, and B. Pearlmutter, "Detecting intrusions using system calls: Alternative data models," in Proc. 1999 IEEE Symp. Security Privacy, Oakland, CA, USA, pp. 133-145.
152. A. Lakhina, M. Crovella, and C. Diot, "Characterization of network-wide anomalies in traffic flows," in Proc. 4th ACM SIGCOMM IMC, Taormina, Italy, 2004, pp. 201-206.
153. Z. Guo, G. Jiang, H. Chen, and K. Yoshihira, "Tracking probabilistic correlation of monitoring data for fault detection in complex systems," in Proc. ICDSN, Philadelphia, PA, USA, 2006, pp. 259-268.
154. G. Jiang, H. Chen, and K. Yoshihira, "Modeling and tracking of transaction flow dynamics for fault detection in complex systems," IEEE Trans. Depend. Secur. Comput., vol. 3, no. 4, pp. 312-326, Oct. 2006.
155. T. Lappas, M. R. Vieira, D. Gunopulos, and V. J. Tsotras, "On the spatiotemporal burstiness of terms," Proc. VLDB, vol. 5, no. 9, pp. 836-847, 2012.
156. M. Mathioudakis, N. Bansal, and N. Koudas, "Identifying, attributing and describing spatial bursts," Proc. VLDB, vol. 3, no. 1, pp. 1091-1102, 2010.
157. C. E. Priebe, J. M. Conroy, D. J. Marchette, and Y. Park, "Scan statistics on Enron graphs," Comput. Math. Org. Theory, vol. 11, no. 3, pp. 229-247, Oct. 2005.
158. M. Gupta, J. Gao, C. C. Aggarwal, and J. Han, "Outlier detection for temporal data," in Proc. 13th SIAM Int. Conf. SDM, 2013.
159. V. Chandola, A. Banerjee, and V. Kumar, "Anomaly detection for discrete sequences: A survey," IEEE Trans. Knowl. Data Eng., vol. 24, no. 5, pp. 823-839, May 2012.
160. M. Markou and S. Singh, "Novelty detection: A Review - Part 1: Statistical approaches," Signal Process., vol. 83, no. 12, pp. 2481-2497, 2003.
161. M. Markou and S. Singh, "Novelty detection: A review - Part 2: Neural network based approaches," Signal Process., vol. 83, no. 12, pp. 2499-2521, 2003.
162. W. Yu, C. C. Aggarwal, S. Ma, and H. Wang, "On anomalous hotspot discovery in graph streams," in Proc. 2013 IEEE ICDM, Dallas, TX, USA.