Spatio-temporal outlier detection in large databases

Proceedings of the International Conference on Information Technology Interfaces, ITI

Volumn , Issue , 2006, Pages 179-184

  Birant, Derya ^a   Kut, Alp ^a  

^a DOKUZ EYLUL UNIVERSITY   (Turkey)

Author keywords

Data mining; Data warehouse; Outlier detection; Spatio temporal data

Indexed keywords

ALGORITHMS; DATA MINING; DATA WAREHOUSES;

OUTLIER DETECTION; SPATIO TEMPORAL DATA;

INTRUSION DETECTION;

EID: 41949101102     PISSN: 13301012     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/iti.2006.1708474     Document Type: Conference Paper

References (21)

1. Abraham T, Roddick JF. Survey of Spatio-Temporal Databases, GeoInformatica (Springer) 1999; 3 (1): 61-99.
2. Adam NR, Janeja VP, Atluri V. Neighbourhood Based Detection of Anomalies in High Dimension Spatio-Temporal Sensor Datasets, ACM Symposium on Applied Computing, Nicosia Cyprus; 2004. p. 576-583.
3. Barnett V, Lewis T. Outliers in Statistical Data. New York: John Wiley; 1994.
4. Breunig MM, Kriegel H-P, Ng R, Sander J. LOF: Identifying Density-Based Local Outliers, ACM SIGMOD Int. Conf. on Management of Data, Dallas, TX; 2000, p. 93-104.
5. nd Int. Conference on Knowledge Discovery and Data Mining, Portland, OR; 1996.
6. Ester M, Kriegel H-P, Sander J, Xu X. Clustering for Mining in Large Spatial Databases, KI-Journal (Artificial Intelligence), Special Issue on Data Mining 1998; 12(1): 18-24.
7. Guha S, Rastogi R, Shim K. CURE: An Efficient Clustering Algorithms for Large Databases, In: Proc. ACM SIGMOD Int. Conf. on Management of Data, Seattle, WA; 1998. p. 73-84.
8. Guttman A. R-trees: A Dynamic Index Structure for Spatial Searching, In: Proceedings of ACM SIGMOD Int. Conf. on Management of Data, Boston, Massachusetts; 1984. p. 47-57.
9. Guting RH. An Introduction to Spatial Database System, VLDB Journal 1994; 3(4): 357-399.
10. Jain A, Murty M, Flynn P. Data Clustering: A Review, ACM Computing Surveys 1999; 31(3): 264-323.
11. Johnson T, Kwok I, Ng R. Fast Computation of 2-Dimensional Depth Contours, In: Proc. 4th. Int. Conf. on KDD, New York, NY; 1998. p. 224-228.
12. Knorr EM, Ng RT. Algorithms for Mining Distance-Based Outliers in Large Datasets, In: Proc. 24th Int. Conf. Very Large Data Bases, New York, NY; 1998. p. 392-403.
13. Knorr EM, Ng RT, Tucakov V. Distance Based Outliers: Algorithms and Applications, Journal: Very Large Data Bases 2000; 8 (3-4): 237-253.
14. Kovács L, Vass D, Vidács A. Improving Quality of Service Parameter Prediction with Preliminary Outlier Detection and Elimination, In: Proc. 2nd Int. Workshop on Inter-Domain Performance and Simulation, Budapest, Hungary; 2004. p. 194-199.
15. th Int. Conf. on Very Large Data Bases, Santiago, Chile; 1994. p. 144-155.
16. Papadimitriou S, Faloutsos C. Cross-Outlier Detection, In: Proc. 8th International Symposium on Spatial and Temporal Databases, Greece; 2003, 199-213.
17. Ruts I, Rousseeuw P. Computing Depth Contours of Bivariate Point Clouds, Journal of Computational Statistics and Data Analysis 1996; 23(1996): 153-168.
18. Samet H. The Design and Analysis of Spatial Data Structures, MA: Addison-Wesley; 1990.
19. Shekhar S, Lu C-T, Zhang P. A Unified Approach to Detecting Spatial Outliers, Geolnformatica, Kluwer Academic Publishers 2003; 7 (2): 139-166.
20. Xu X, Ester M, Kriegel H-P, Sander J. A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases, In: Proceedings of IEEE International Conference on Data Engineering, Orlando, Florida; 1998. p. 324-331.
21. Topex/Poseidon Satellite Description http://podaac.jpl.nasa.gov/woce/[15/ 09/2005]