Rough sets, kernel set, and spatiotemporal outlier detection

IEEE Transactions on Knowledge and Data Engineering

Volumn 26, Issue 1, 2014, Pages 194-207

  Albanese, Alessia ^a   Pal, Sankar K ^b   Petrosino, Alfredo ^a  

^a UNIVERSITY OF NAPLES PARTHENOPE   (Italy)

^b INDIAN STATISTICAL INSTITUTE   (India)

Author keywords

Outlier detection; Rough set and granular computing; Spatiotemporal data; Spatiotemporal uncertainty management

Indexed keywords

KNOWLEDGE DISCOVERY AND DATA MININGS; LOWER AND UPPER APPROXIMATIONS; OUTLIER DETECTION; ROUGH SET APPROXIMATION; ROUGH-FUZZY CLUSTERING; SPATIO-TEMPORAL DATA; SPATIOTEMPORAL OUTLIER DETECTION; UNCERTAINTY MANAGEMENT;

APPROXIMATION ALGORITHMS; DATA MINING; FUZZY CLUSTERING; STATISTICS; VIRTUAL REALITY; WIRELESS SENSOR NETWORKS;

ROUGH SET THEORY;

EID: 84890404816     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2012.234     Document Type: Article

References (57)

1. C.C. Aggarwal and P. Yu, "Finding Generalized Projected Clusters in High Dimensional Spaces, " Proc. ACM SIGMOD Int'l Conf. Management Data, pp. 70-81, 2000.
2. C.C. Aggarwal and P.S. Yu, "An Effective and Efficient Algorithm for High-Dimensional Outlier Detection, " VLDB J., vol. 14, pp. 211-221, 2005. (Pubitemid 40536894)
3. A. Albanese and A. Petrosino, "A Non Parametric Approach to the Outlier Detection in Spatio-Temporal Data Analysis, " Information Technology and Innovation Trends in Organizations, D'Atri, et al., eds., pp. 101-108, Springer Verlag, 2011.
4. F. Angiulli and C. Pizzuti, "Outlier Mining in Large High- Dimensional Data Sets, " IEEE Trans. Knowledge and Data Eng., vol. 17, no. 2, pp. 203-215, Feb. 2005. (Pubitemid 40320179)
5. F. Angiulli and F. Fassetti, "Distance-Based Outlier Queries in Data Streams: The Novel Task and Algorithms, " J. Data Mining and Knowledge Discovery, vol. 20, no. 2, pp. 290-324, 2010.
6. M. Ankerst, M.M. Breunig, H.-P. Kriegel, and J. Sander, "Optics: Ordering Points To Identify The Clustering Structure, " Proc. ACM SIGMOD Int'l Conf. Management Data (SIGMOD '99), pp. 49-60, 1999. (Pubitemid 129597323)
7. V. Barnett and T. Lewis, Outliers in Statistical Data. John Wiley & Sons, 1994.
8. S.D. Bay, "The UCI KDD Repository, " http://kdd.ics.uci.edu, 1999.
9. D. Birant and A. Kut, "Spatio-Temporal Outlier Detection in Large Databases, " J. Computing and Information Technology, vol. 14, no. 4, pp. 291-297, 2006.
10. T. Bittner, "Rough Sets in Spatio-Temporal Data Mining, " Proc. First Int'l Workshop Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers (TSDM '00), pp. 89-104, 2000.
11. S. Boriah, V. Chandola, and V. Kumar, "Similarity Measures for Categorical Data: A Comparative Evaluation, " Proc. Eighth SIAM Int'l Conf. Data Mining, pp. 243-254, 2008.
12. M.M. Breunig, H-P. Kriegel, R.T. Ng, and J. Sander, "LOF: Identifying Density Based Local Outliers, " Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 93-104, 2000.
13. A. Ceglar, J.F. Roddick, and D.M.W. Powers, "CURIO: A Fast Outlier and Outlier Cluster Detection Algorithm for Large Data Sets, " Proc. Second Int'l Workshop Integrating Artificial Intelligence and Data Mining, pp. 37-45, 2007.
14. V. Chandola, A. Banerjee, and V. Kumar, "Anomaly Detection: A Survey, " ACM Computing Surveys, vol. 41, no. 3, pp. 15:1-15:58, 2009.
15. Y. Chen, D. Miao, and R. Wang, "Outlier Detection Based on Granular Computing, " Proc. Sixth Int'l Conf. Rough Sets and Current Trends Computing, pp. 283-292, 2008.
16. Y. Chen, D. Miao, and H. Zhang, "Neighborhood Outlier Detection, " Expert Systems with Applications, vol. 37, no. 12, pp. 8745-8749, 2010.
17. T. Cheng and Z. Li, "A Multiscale Approach to Detect Spatio- Temporal Outliers, " Trans. GIS, vol. 10, no. 2, pp. 253-263, 2006.
18. K. Das and J. Schneider, "Detecting Anomalous Records in Categorical Data Sets, " Proc. 13th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 220-229, 2007. (Pubitemid 350229209)
19. E. Frentzos, K. Gratsias, N. Pelekis, and Y. Theodoridis, "Nearest Neighbor Search on Moving Object Trajectories, " Proc. Ninth Int'l Symp. Spatial and Temporal Databases (SSTD '05), pp. 328-345, 2005. (Pubitemid 41433173)
20. A.K. Ghosh and P. Chaudhuri, "On Maximum Depth Classifiers, " Scandinavian J. Statistics, vol. 32, no. 2, pp. 327-350, 2005.
21. S. Guha, R. Rastogi, and K. Shim, "CURE: An Efficient Clustering Algorithm for Large Databases, " Proc. ACM SIGMOD Int'l Conf. Management Data, vol. 27, no. 2, pp. 73-84, 1998. (Pubitemid 128655958)
22. J.M.P. Gutierrez and J.F. Gregori, Clustering Techniques Applied to Outlier Detection of Financial Market Series Using a Moving Window Filtering Algorithm, Unpublished working paper series, no. 948, European Central Bank, pp. 1-45, 2008.
23. S. Harkins, H.X. He, G.J. Willams, and R.A. Baxter, "Outlier Detection Using Replicator Neural Networks, " Proc. Fourth Int'l Conf. Data Warehousing and Knowledge Discovery, pp. 170-180, 2002.
24. Z. He, X. Xu, and S. Deng, "Discovering Cluster-Based Local Outliers, " J. Pattern Recognition Letters, vol. 24, pp. 1641-1650, 2003.
25. V. Hodge and J. Austin, "A Survey of Outlier Detection Methodologies, " J. Artificial Intelligence Rev., vol. 22, no. 2, pp. 85-126, 2004.
26. F. Ingelrest, G. Barrenetxea, G. Schaefer, M. Vetterli, O. Couach, and M. Parlange, "SensorScope: Application-Specific Sensor Network for Environmental Monitoring, " J. ACM Trans. Sensor Networks, vol. 6, no. 2, pp. 1-32, 2010.
27. F. Jiang, Y. Sui, and C. Cao, "Outlier Detection Based on Rough Membership Function, " Proc. Fifth Int'l Conf. Rough Sets and Current Trends Computing (RSCTC '06), pp. 388-397, 2006. (Pubitemid 44860567)
28. F. Jiang, Y. Sui, and C. Cao, "Some Issues about Outlier Detection in Rough Set Theory, " Expert Systems with Applications, vol. 36, no. 3, pp. 4680-4687, 2009.
29. R. Jornsten, "Clustering and Classification Based on the L1 Data Depth, " J. Multivariate Analysis, vol. 90, no. 1, pp. 67-89, 2004. (Pubitemid 41137007)
30. T. Johnson, I. Kwok, and R.T. Ng, "Fast Computation of 2- Dimensional Depth Contours, " Proc. Fourth Int'l Conf. Knowledge Discovery and Data Mining, pp. 224-228, 1998.
31. E. Knorr and R. Ng, "Algorithms for Mining Distance-Based Outliers in Large Data Sets, " Proc. 24th Int'l Conf. Very Large Data Bases (VLDB '98), pp. 392-403, 1998.
32. A. Koufakou and M. Georgiopoulos, "A Fast Outlier Detection Strategy for Distributed High-Dimensional Data Sets with Mixed Attributes, " Data Mining and Knowledge Discovery, vol. 20, no. 2, pp. 259-289, 2010.
33. J. Laurikkala, M. Juhola, and E. Kentala, "Informal Identification of Outliers in Medical Data, " Proc. Fifth Workshop Intelligent Data Analysis Medicine Pharmacology (IDAMAP), pp. 20-24, 2000.
34. W. Liu, Y. Zheng, S. Chawla, J. Yuan, and X. Xie, "Discovering Spatio-Temporal Causal Interactions in Traffic Data Streams, " Proc. 17th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 1010-1018, 2011.
35. P. Maji and S.K. Pal, "Rough Set Based Generalized Fuzzy CMeans Algorithm and Quantitative Indices, " IEEE Trans. Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 37, no. 6, pp. 1529- 1540, Dec. 2007. (Pubitemid 350201160)
36. M. Markos and S. Sameer, "Novelty Detection: A Review Part 1: Statistical Approaches, " Signal Processing, vol. 83, no. 12, pp. 2481- 2497, 2003.
37. E. Muller, I. Assent, U. Steinhausen, and T. Seidl, "OutRank: Ranking Outliers in High Dimensional Data, " Proc. IEEE 24th Int'l Conf. Data Eng. Workshop, pp. 600-603, 2008.
38. R.T. Ng and J. Han, "CLARANS: A Method for Clustering Objects for Spatial Data Mining, " IEEE Trans. Knowledge and Data Eng., vol. 14, no. 5, pp. 1003-1016, Sept./Oct. 2002. (Pubitemid 35242611)
39. T.T. Nguyen, "Outlier Detection: An Approximate Reasoning Approach, " Proc. Int'l Conf. Rough Sets and Intelligent Systems Paradigms (RSEISP '07), pp. 495-504, 2007.
40. S. Papadimitriou, H. Kitagawa, P.B. Gibbons, and C. Faloutsos, "LOCI: Fast Outlier Detection Using the Local Correlation Integral, " Proc. 19th Int'l Conf. Data Eng. (ICDE '03), pp. 315-326, 2003.
41. Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer, 1991.
42. Z. Pawlak and A. Skowron, "A Rough Set Approach for Decision Rules Generation, " Proc. Workshop W12: The Management Uncertainty in AI at 13th IJCAI, 1993.
43. S. Ramaswamy, R. Rastogi, and K. Shim, "Efficient Algorithms for Mining Outliers from Large Data Sets, " Proc. ACM SIGMOD Int'l Conf. Management Data, pp. 427-438, 2000.
44. N.N.R. Ranga Suri, N. Murty, and G. Athithan, "Data Mining Techniques for Outlier Detection, " Visual Analytics and Interactive Technologies: Data, Text and Web Mining Applications, chapter 2, pp. 22-38, IGI Global Snippet, 2010.
45. J. Sander, M. Ester, H.-P. Kriegel, and X. Xu, "Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications, " Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 169-194, 1998.
46. P. Sun and S. Chawla, "On Local Spatial Outliers, " Proc. IEEE Fourth Int'l Conf. Data Mining, pp. 209-216, Nov. 2004. (Pubitemid 40731033)
47. Y. Tao, X. Xiao, and S. Zhou, "Mining Distance-Based Outliers from Large Databases in Any Metric Space, " Proc. 12th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 394- 403, 2006. (Pubitemid 44535536)
48. P.M. Valero Mora, F.W. Young, and M. Friendly, "Visualizing Categorical Data in ViSta, " Computational Statistics Data Analysis, vol. 43, no. 4, pp. 495-508, 2003. (Pubitemid 36990917)
49. K. Venkateswara Rao, A. Govardhan, and K.V. Chalapati Rao, "Spatio Temporal Data Mining: Issues, Task and Applications, " Int'l J. Computer Science Eng. Survey, vol. 3, no. 1, pp. 39-52, 2012.
50. X.R. Wang, J.T. Lizier, O. Obst, M. Prokopenko, and P. Wang, "Spatiotemporal Anomaly Detection in Gasmonitoring Sensor Networks, " Proc. European Conf. Wireless Sensor Networks (EWSN), pp. 90-105, 2008.
51. G.J. Willams, R.A. Baxter, H.X. He, S. Harkins, and L.F. Gu, "A Comparative Study of RNN for Outlier Detection in Data Mining, " Proc. IEEE Int'l Conf. Data Mining (ICDM '03), pp. 709- 712, 2002.
52. E. Wu, W. Liu, and S. Chawla, "Spatio-Temporal Outlier Detection in Precipitation Data, " Proc. Second Int'l Conf. Knowledge Discovery from Sensor Data, pp. 115-133, 2008.
53. Y.Y. Yao, "Two Views of the Theory of Rough Sets in Finite Universes, " Int'l J. Approximate Reasoning, vol. 15, pp. 291-317, 1996. (Pubitemid 126414908)
54. T. Zhang, R. Ramakrishnan, and M. Livny, "BIRCH: An Efficient Data Clustering Method for Very Large Databases, " Proc. ACM SIGMOD Int'l Conf. Management Data, vol. 25, no. 2, pp. 103-114, 1996. (Pubitemid 126440760)
55. Y. Zhang, S. Yang, and Y. Wang, "LDBOD: A Novel Local Distribution Based Outlier Detector, " Pattern Recognition Letters, vol. 29, no. 7, pp. 967-976, 2008.
56. Y. Zhang, N.A.S. Hamm, N. Meratnia, A. Stein, M. van de Voort, and P.J.M. Havinga, "Statistics-Based Outlier Detection for Wireless Sensor Networks, " Int'l J. Geographical Information Science, vol. 26, no. 8, pp. 1373-1392, 2012.
57. C. Zhu, H. Kitagawa, and C. Faloutsos, "Example-Based Robust Outlier Detection in High Dimensional Data Sets, " Proc. IEEE Fifth Int'l. Conf. Data Mining (ICDM '05), pp. 829-832, Nov. 2005. (Pubitemid 47385807)