Fast distributed outlier detection in mixed-attribute data sets

Data Mining and Knowledge Discovery

Volumn 12, Issue 2-3, 2006, Pages 203-228

  Otey, Matthew Eric ^a   Ghoting, Amol ^a   Parthasarathy, Srinivasan ^a  

^a Ohio State University   (United States)

Author keywords

Anomaly detection; Data streams; Distributed data mining; Mining dynamic data; Mixed attribute data sets; Outlier detection

Indexed keywords

ALGORITHMS; DATA MINING; DIAGNOSIS; DISTRIBUTED COMPUTER SYSTEMS; INFORMATION SCIENCE; INFORMATION TECHNOLOGY; PARAMETER ESTIMATION; PROBLEM SOLVING;

ANOMALY DETECTION; DATA STREAMS; DISTRIBUTED DATA MINING; MINING DYNAMIC DATA; MIXED-ATTRIBUTE DATA SETS; OUTLIER DETECTION;

DATABASE SYSTEMS;

EID: 33646553013     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-005-0014-6     Document Type: Article

References (30)

1. Agrawal, R. and Srikant, R. 1994, Fast algorithms for mining association rules. In Proc, of the International Conference on Very Large Data Bases VLDB, Morgan Kaufmann, pp. 487-499.
2. Barnett, V. and Lewis, T. 1994. Outliers in Statistical Data. John Wiley.
3. Bay, S.D. and Schwabacher, M. 2003. Mining distance-based outliers in near linear time with randomization and a simple pruning rule. Proc. of ACM SIGKDD Inte'l Conf. on Knowledge Discovery and Data Mining.
4. Blake, C. and Merz, C. 1998. UCI machine learning repository.
5. Bolton, R.J. and Hand, D.J. 2002. Statistical fraud detection: A review. Statistical Science, 17:235-255.
6. Breunig, M.M., Kriegel, H.-P., Ng, R.T., and Sander, J. 2000. LOF: Identifying density-based local outliers. Proc. of the ACM SIGMOD International Conference on Management of Data.
7. Gamberger, D., Lavrač, N., and Grošelj, C. 1999. Experiments with noise filtering in a medical domain. Proc. of the International Conference on Machine Learning.
8. Ghoting, A., Otey, M.E., and Parthasarathy, S. 2004. Loaded: Link-based outlier and anomaly detection in evolving data sets. Proc. of the IEEE International Conference on Data Mining.
9. Guha, S., Rastogi, R., and Shim, K. 2000. ROCK: A robust clustering algorithm for categorical attributes. Information Systems, 25:345-366.
10. Hettich, S. and Bay, S. 1999. KDDCUP 1999 dataset, UCI KDD archive.
11. Huang, Y.-A. and Lee, W. 2003. A cooperative intrusion detection system for ad hoc networks. Proc. of the ACM workshop on Security of ad hoc and sensor networks (SASN), Fairfax, Virginia: ACM Press, pp. 135-147.
12. Jain, A.K. and Dubes, R.C. 1988. Algorithms for Clustering Data. Prentice Hall.
13. Johnson, T., Kwok, I., and Ng, R. 1998. Fast computation of 2-dimensional depth contours. Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
14. Knorr, E., Ng, R., and Tucakov, V. 2000. Distance-based outliers: Algorithms and applications. VLDB Journal.
15. Knorr, E. and Ng, R.T. 1998. Algorithms for mining distance-based outliers in large datasets. Proc. of the International Conference on Very Large Databases.
16. Lazarevic, A., Ertoz, L., Ozgur, A., Kumar, V., and Srivastava, J. 2003. A comparative study of outlier detection schemes for network intrusion detection. Proc. of the SIAM International Conference on Data Mining.
17. Locasto, M.E., Parekh, J.J., Stolfo, S.J., Keromytis, A.D., Malkin, T., and Misra, V. 2004. Collaborative distributed intrusion detection (Technical Report CUCS-012-04). Department of Computer Science, Columbia University, New York.
18. Mahoney, M.V. and Chan, P.K. 2002. Learning nonstationary models of normal network traffic for detecting novel attacks. Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
19. Otey, M., Parthasarathy, S., Ghoting, A., Li, G., Narravula, S., and Panda, D. 2003. Towards nic-based intrusion detection. Proc. of 9th annual ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
20. Palpanas, T., Papadopoulos, D., Kalogeraki, V., and Gunopulos, D. 2003. Distributed deviation detection in sensor networks. SIGMOD Record, 32:77-82.
21. Papadimitriou, S., Kitawaga, H., Gibbons, P.B., and Faloutsos, C. 2003. LOCI: Fast outlier detection using the local correlation integral. Proc. of the International Conference on Data Engineering.
22. Penny, K.I. and Jolliffe, IT. 2001. A comparison of multivariate outlier detection methods for clinical laboratory safety data. The Statistician, Journal of the Royal Statistical Society, 50:295-308.
23. Porras, P.A. and Neumann, P.G. 1997. EMERALD: Event monitoring enabling responses to anomalous live disturbances. Proc. of the 20th NIST-NCSC National Information Systems Security Conference, pp. 353-365.
24. Rice, J. 1995. Mathematical statistics and data analysis. Duxbury Press.
25. Sequeira, K. and Zaki, M. 2002. ADMIT: Anomaly-based data mining for intrusions. Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
26. Veloso, A.A., Meira W., Jr., de Carvalho, M.B., Possas, B., Parthasarathy, S., and Zaki, M.J. 2002. Mining frequent itemsets in evolving databases. Proc. of the SIAM International Conference on Data Mining.
27. Wu, X. and Zhang, S. 2003. Synthesizing high-frequency rules from different data sources. IEEE Transactions on Knowledge and Data Engineering, 15:353-367.
28. Zhang, S., Wu, X., and Zhang, C. 2003a. Multi-database mining. IEEE Computational Intelligence Bulletin, 2:5-13.
29. Zhang, Y. and Lee, W. 2000. Intrusion detection in wireless ad-hoc networks. Mobile Computing and Networking, pp. 275-283.
30. Zhang, Y., Lee, W., and Huang, Y.-A. 2003b. Intrusion detection techniques for mobile wireless networks. Wireless Networks, 9:545-556.