Mining outliers with ensemble of heterogeneous detectors on random subspaces

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

Volumn 5981 LNCS, Issue PART 1, 2010, Pages 368-383

  Nguyen, Hoang Vu ^a   Ang, Hock Hee ^a   Gopalkrishnan, Vivekanand ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ABNORMAL BEHAVIOR; DETECTION PROBLEMS; DETECTION TECHNIQUE; EMPIRICAL STUDIES; NON-TRIVIAL TASKS; OUTLIER DETECTION; OUTLIER DETECTION ALGORITHM; RANDOM SUBSPACES; REAL-WORLD; UNIFIED FRAMEWORK;

DATABASE SYSTEMS;

DETECTORS;

EID: 78650506461     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-12026-8_29     Document Type: Conference Paper

References (18)

1. Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: Identifying density-based local outliers. In: SIGMOD, pp. 93-104 (2000)
2. Papadimitriou, S., Kitagawa, H., Gibbons, P.B., Faloutsos, C.: LOCI: Fast outlier detection using the local correlation integral. In: ICDE, pp. 315-324 (2003)
3. Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: VLDB, pp. 392-403 (1998)
4. Angiulli, F., Basta, S., Pizzuti, C.: Distance-based detection and prediction of outliers. IEEE Transactions on Knowledge and Data Engineering 18(2), 145-160 (2006)
5. Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: SIGMOD, pp. 427-438 (2000)
6. Aggarwal, C.C., Yu, P.S.: An effective and efficient algorithm for high-dimensional outlier detection. VLDB J. 14(2), 211-221 (2005)
7. Lazarevic, A., Ertöz, L., Kumar, V., Ozgur, A., Srivastava, J.: A comparative study of anomaly detection schemes in network intrusion detection. In: SDM (2003)
8. Beyer, K.S., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is "nearest neighbor" meaningful? In: ICDT, pp. 217-235 (1999)
9. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832-844 (1998)
10. Lazarevic, A., Kumar, V.: Feature bagging for outlier detection. In: KDD, pp. 157-166 (2005)
11. Kong, E.B., Dietterich, T.G.: Error-correcting output coding corrects bias and variance. In: ICML, pp. 313-321 (1995)
12. He, Z., Deng, S., Xu, X.: A unified subspace outlier ensemble framework for outlier detection. In: Fan, W., Wu, Z., Yang, J. (eds.) WAIM 2005. LNCS, vol. 3739, pp. 632-637. Springer, Heidelberg (2005)
13. Abe, N., Zadrozny, B., Langford, J.: Outlier detection by active learning. In: KDD, pp. 504-509 (2006)
14. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119-139 (1997)
15. Gao, J., Tan, P.N.: Converting output scores from outlier detection algorithms into probability estimates. In: ICDM, pp. 212-221 (2006)
16. Strehl, A., Ghosh, J.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3, 583-617 (2003)
17. Otey, M.E., Ghoting, A., Parthasarathy, S.: Fast distributed outlier detection in mixed-attribute data sets. Data Mining and Knowledge Discovery 12(2-3), 203-228 (2006)
18. Hawkins, D.M.: Identification of Outliers. Chapman and Hall, London (1980)