Outlier detection with globally optimal exemplar-based GMM

Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics

Volumn 1, Issue , 2009, Pages 144-153

  Yang, Xingwei ^a   Latecki, Longin Jan ^a   Pokrajac, Dragoljub ^b  

^a TEMPLE UNIVERSITY   (United States)

^b DELAWARE STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER CENTERS; DATA MINING APPLICATIONS; DATA POINTS; DATA SETS; EXPECTATION-MAXIMIZATION ALGORITHMS; GAUSSIAN MIXTURE MODEL; GAUSSIANS; GLOBAL PROPERTIES; MIXTURE PROPORTIONS; OUTLIER DETECTION; REAL LIFE DATASETS; UNSUPERVISED ALGORITHMS; WEIGHTED SUM;

IMAGE SEGMENTATION; MAGNETOSTRICTIVE DEVICES; OPTIMIZATION;

MIXTURES;

EID: 72849121726     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (36)

1. M. Joshi, R. Agarwal, V. Kumar and P. Nrule, Mining Needles in a Haystack: Classifying Rare Classes via Two-Phase Rule Indection, Proceedings of the ACM SIGMOD Conference, 2001.
2. N. Billor, A. Hadi and P. Velleman, BACON: Blocked Adaptive Computationally-Efficient Outlier Nominators, Computational Statistics and Data Analysis., 34 (2000), pp. 279-298.
3. E. Eskin, Anomaly Detection over Noisy Data using Learned Probability Distributions, ICML, 2000.
4. S. Ramaswamy, R. Rastogi and K. Shim, Efficient algorithms for mining outliers from large data sets, Proceedings of the ACM SIGMOD Conference, 2000.
5. E. Knorr and R. Ng, Algorithms for Mining Distance based Outliers in Large Data Sets, VLDB, 1998.
6. C. C. Aggarwal and P. Yu, Outlier detection for high dimensional data, Proceedings of the ACM SIGMOD Conference, 2001.
7. M. M. Breunig, H. P. Kriegel, R. T. Ng and J. Sander, LOF: Identifying Density Based Local Outliers, Proceedings of the ACM SIGMOD Conference, 2000.
8. S. Papadimitriou, H. Kitagawa, P. B. Gibbons and C. Faloutsos, LOCI: Fast Outlier Detection Using the Local Correlation Integral, ICDE, 2003.
9. D. Yu, G. Sheikholeslami and A. Zhang, FindOut: Finding Outliers in Very Large Datasets, The Knowledge and Information Systems (KAIS), 4 (2002), pp. 387-412.
10. S. Hawkins, H. He, G. Williams and R. Baxter, Outlier Detection Using Replicator Neural Networks, Proc. of the 4th Int. Conf. on Data Warehousing and Knowledge Discovery (DaWaK02), 2002.
11. A. Lazarevic, L. Ertoz, A. Ozgur, J. Srivastava and V. Kumar, A comparative study of anomaly detection schemes in network intrusion detection, SDM, 2003.
12. D. Lashkari and P. Golland, Convex Clustering with Exemplar-Based Models, Advances in Neural Information Processing Systems, 2007.
13. J. Tang, Z. Chen, A. Fu and D. Cheung, Enhancing Effectiveness of Outlier Detections for Low Density Patterns, PAKDD, 2002.
14. S. Subramaniam, T. Palpanas, D. Papadopoulos, V. Kalogeraki and D. Gunopulos, Online Outlier Detection in Sensor Data Using Non-Parametric Models, VLDB, 2006.
15. A. Lazarevic and V. Kumar, Feature Bagging for Outlier Detection, KDD, 2005.
16. D. M. J. TAX, One-class classification: concept-learning in the absence of counter-examples, PHD Thesis, 2001.
17. M. Ester, H.-P Kriegel, J. Sander and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, KDD, 1996
18. M. F. Jaing, S. S. Tseng and C. M. Su, Two-phase clustering process for outliers detection, Pattern Recognition Letters, 22 (2001), pp. 691-700.
19. M. V. Mahoney and P. K. Chan, Learning rules for anomaly detection of hostile network traffic, ICDM, 2003.
20. Z. He, X. Xu and S. Deng, Discovering cluster-based local outliers, Pattern Recognition Letters, 24 (2003), pp. 1641-1650.
21. Z. He, X. Xu and S. Deng, Squeezer: An Efficient Algorithm for Clustering Categorical Data, Journal of Computer Science and Technology, 17 (2003), pp. 611-624.
22. D. Barbara, C. Domeniconi and J. P. Rogers, Detecting Outliers using Transduction and Statistical Testing, KDD, 2006.
23. M. Ankerst, M. M. Breunig, H. Kriegel and J. Sander, OPTICS: Ordering Points To Identify the Clustering Structure, SIGMOD, 1999.
24. M. M. Breunig, H. P. Kriegel, R. T. Ng and J. Sander, Optics-of: Identifying local outliers, Proceedings of the third European Conference on Principles of Data Mining and Knowledge Discovery, 1999.
25. V. Chandola, A. Banerjee and V. Kumar, Outlier Detection - A Survey, ACM Computing Surveys, to appear.
26. A. Banerjee, S. Merugu, I. S. Dhillon and J. Ghosh, Clustering with Bregman Divergences, Journal of Machine Learning Research, 6 (2005), pp. 1705-1749.
27. B. Fischer and J. M. Buhmann, Path-based clustering for grouping of smooth curves and texture segmentation, IEEE. PAMI, 25 (2003), pp. 513-518.
28. B. Fischer, V. Roth and J. M. Buhmann, Clustering with the connectivity kernel, Advances in Neural Information Processing Systems, 2004.
29. L. Wei, E. Keogh and X. Xi, SAXually Explicit Images: Finding Unusual Shapes, ICDM, 2006.
30. H. Ling and D.W. Jacobs, Shape Classification Using the Inner-Distance, IEEE. PAMI, 29 (2007), pp. 286-299.
31. L. J. Latecki, R. Lakämper and U. Eckhardt, Shape Descriptors for Non-rigid Shapes with a Single Closed Contour, CVPR, 2000.
32. N. Chawla, A. Lazarevic, L. Hall and K. Bowyer, SMOTEBoost: Improving the Prediction of Minority Class in Boosting, PKDD, 2003.
33. F. Provost and T. Fawcett, Robust Classification for Imprecise Environments, Machine Learning, 42 (2001), pp. 203-231.
34. S. D. Bay and M. Schwabacher. Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule, KDD, 2003.
35. A. Ghoting, S. Parthasarathy and M. E. Otey Fast mining of distance-based outliers in high-dimensional datasets, Data Mining and Knowledge Discovery, 16 (2008), pp. 349-364.
36. F. Angiulli and F. Fassetti. Very efficient mining of disance-based outliers, Proc. of 16th ACM Conf. on Information and Knowledge Management, 2007.