Ranking outliers using symmetric neighborhood relationship

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3918 LNAI, Issue , 2006, Pages 577-593

  Jin, Wen ^a   Tung, Anthony K H ^b   Han, Jiawei ^c   Wang, Wei ^d  

^a SIMON FRASER UNIVERSITY   (Canada)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c University of Illinois at Urbana Champaign   (United States)

^d FUDAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSICAL OUTLIER ANALYSIS ALGORITHMS; DENSITY DISTRIBUTION; NEIGHBORHOOD RELATIONSHIP;

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; DATABASE SYSTEMS; EVALUATION;

DATA MINING;

EID: 33745772192     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11731139_68     Document Type: Conference Paper

References (26)

1. C. Aggarwal and P. Yu: Outlier Detection for High Dimensional Data. SIGMOD 2001
2. M. M. Breunig, H.P. Kriegel, R.T. Ng, and J.Sander: LOF: Identifying Density-based Local Outliers. SIGMOD 2000
3. D. Chakrabarti: AutoPart: Parameter-Free Graph Partitioning and Outlier Detection. PKDD 2004
4. Z. X. Chen, A. W. Fu, J. Tang: On Complementarity of Cluster and Outlier Detection Schemes. DaWaK 2003
5. A. L. Chiu, A. W. Fu: Enhancements on Local Outlier Detection, IDEAS 2003
6. M. Ester, H. P. Kriegel et al.: A Density-based Algorithm for Discovering Clusters in Large Spatial Databases. KDD 1996
7. S. Guha, R. Rastogi, and K.Shim: Cure: An Efficient Clustering Algorithm for Large Databases. SIGMOD 1998
8. V. Hautamki, I. Krkkinen and P. Frnti: Outlier Detection Using k-nearest Neighbour Graph, ICPR 2004.
9. J. W. Han, M. Kamber: Data Mining: Concepts and Techniques. In Morgan Kaufmann Publishers.
10. H. Jagadish, N. Koudas, and S. Muthukrishnan: Mining Deviants in a Time Series Database. VLDB 1999
11. W. Jin, K. H. Tung and J. W. Han: Mining Top-n Local Outliers in Large Databases. KDD 2001
12. E. Knorr, R. Ng: Algorithms for Mining Distance-Based Outliers in Large Datasets. VLDB 1998
13. E. Knorr and R. Ng: Finding Intensional Knowledge of Distance-Based Outliers. VLDB 1999
14. F. Korn and S. Muthukrishnan: Influence Sets Based on Reverse Nearest Neighbor Queries. SIGMOD 2000
15. S. Muthukrishnan, R. Shah, J. S. Vitter: Mining Deviants in Time Series Data Streams. SSDBM 2004
16. R. Ng and J. W. Han: Efficient and Effective Clustering Method for Spatial Data Mining. VLDB 1994
17. S. Papadimitriou, H. Kitagawa et al. LOCI: Fast Outlier Detection Using the Local Correlation Integral. ICDE 2003
18. S. Papadimitriou, C. Faloutsos: Cross-Outlier Detection. SSTD 2003
19. N. Roussopoulos, S. Kelley and F. Vincent: Nearest neighbor queries. SIGMOD 1995
20. S. Ramaswamy, R. Rastogi, K. Shim: Efficient Algorithms for Mining Outliers from Large Data Sets. SIGMOD 2000
21. S. Shekhar, C. T. Lu, P. S. Zhang: Detecting Graph-based Spatial Outliers. KDD 2001
22. J. Tang, Z. X. Chen et al.: Enhancing Effectiveness of Outlier Detections for Low Density Patterns. PAKDD 2002
23. W. K. Wong, A. W. Moore et al.: Rule-Based Anomaly Pattern Detection for Detecting Disease Outbreaks. AAAI 2002
24. M. L. Yiu, N. Mamoulis: Clustering Objects on a Spatial Network. SIGMOD 2004
25. M. L. Yiu et al.: Aggregate Nearest Neighbor Queries in Road Networks. IEEE Trans. Knowl. Data Eng. 17(6), 2005
26. T. Zhang et al.: BIRCH: An Efficient Data Clustering Method for Very Large Databases. SIGMOD 1996