Outlier ranking via subspace analysis in multiple views of the data

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2012, Pages 529-538

  Müller, Emmanuel ^a,b   Assent, Ira ^c   Iglesias, Patricia ^a   Mülle, Yvonne ^a   Böhm, Klemens ^a  

^a KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

^b UNIVERSITY OF ANTWERP   (Belgium)

^c AARHUS UNIVERSITY   (Denmark)

Author keywords

Clusterings; Multiple subspaces; Outlier ranking

Indexed keywords

ATTRIBUTE SETS; CLUSTERINGS; EXPERIMENTAL EVALUATION; HIGH QUALITY; MULTIPLE SUBSPACES; MULTIPLE VIEWS; OUTLIER MINING; OUTLIER RANKING; SUBSPACE ANALYSIS;

DATA MINING; QUALITY CONTROL;

STATISTICS;

EID: 84874104427     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2012.112     Document Type: Conference Paper

References (30)

1. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, "Automatic subspace clustering of high dimensional data for data mining applications," in SIGMOD, 1998, pp. 94-105.
2. C. C. Aggarwal, J. L. Wolf, P. S. Yu, C. Procopiuc, and J. S. Park, "Fast algorithms for projected clustering," in SIGMOD, 1999, pp. 61-72.
3. M. L. Yiu and N. Mamoulis, "Frequent-pattern based iterative projected clustering," in ICDM, 2003, pp. 689-692.
4. K. Sequeira and M. Zaki, "SCHISM: A new approach for interesting subspace mining," in ICDM, 2004, pp. 186-193.
5. H.-P. Kriegel, P. Kröger, M. Renz, and S. Wurst, "A generic framework for efficient subspace clustering of highdimensional data," in ICDM, 2005, pp. 250-257.
6. I. Assent, R. Krieger, E. Müller, and T. Seidl, "INSCY: Indexing subspace clusters with in-process-removal of redundancy," in ICDM, 2008, pp. 719-724.
7. G. Moise and J. Sander, "Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering," in KDD, 2008, pp. 533-541.
8. E. Müller, I. Assent, S. Günnemann, R. Krieger, and T. Seidl, "Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data," in ICDM, 2009, pp. 377-386.
9. K. Sim, V. Gopalkrishnan, A. Zimek, and G. Cong, "A Survey on Enhanced Subspace Clustering," DMKD, 2012.
10. E. Müller, S. Günnemann, I. Assent, and T. Seidl, "Evaluating clustering in subspace projections of high dimensional data," PVLDB, vol. 2, no. 1, pp. 1270-1281, 2009.
11. C. C. Aggarwal and P. S. Yu, "Outlier detection for high dimensional data," in SIGMOD, 2001, pp. 37-46.
12. H.-P. Kriegel, E. Schubert, A. Zimek, and P. Kröger, "Outlier detection in axis-parallel subspaces of high dimensional data," in PAKDD, 2009, pp. 831-838.
13. E. Müller, M. Schiffer, and T. Seidl, "Statistical selection of relevant subspace projections for outlier ranking," in ICDE, 2011, pp. 434-445.
14. E. Müller, I. Assent, U. Steinhausen, and T. Seidl, "OutRank: ranking outliers in high dimensional data," in ICDE Workshops, DBRank. IEEE, 2008, pp. 600-603.
15. P. Rousseeuw and A. Leroy, Robust Regression and Outlier Detection. Wiley, 1987.
16. V. Chandola, A. Banerjee, and A. Kumar, "Anomaly detection: A survey," ACM Computing Surveys, Vol. 41, No.3, July 2009.
17. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, "A densitybased algorithm for discovering clusters in large spatial databases," in Proc. KDD, 1996, pp. 226-231.
18. M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander, "LOF: Identifying density-based local outliers," in SIGMOD, 2000, pp. 93-104.
19. H.-P. Kriegel, M. Schubert, and A. Zimek, "Angle-based outlier detection in high-dimensional data," in Proc. ACM SIGKDD, 2008.
20. A. Zimek, E. Schubert, and H.-P. Kriegel, "A survey on unsupervised outlier detection in high-dimensional numerical data," Statistical Analysis and Data Mining, 2012.
21. S. Papadimitriou, H. Kitagawa, P. Gibbons, and C. Faloutsos, "LOCI: Fast outlier detection using the local correlation integral," in ICDE, 2003, pp. 315-326.
22. I. Joliffe, Principal Component Analysis. Springer, New York, 1986.
23. A. Lazarevic and V. Kumar, "Feature bagging for outlier detection," in KDD, 2005, pp. 157-166.
24. T. Fawcett, "An introduction to roc analysis," Pattern recognition letters, vol. 27, no. 8, pp. 861-874, 2006.
25. E. Müller, M. Schiffer, P. Gerwert, M. Hannen, T. Jansen, and T. Seidl, "SOREX: Subspace outlier ranking exploration toolkit," in ECML PKDD, 2010, pp. 607-610.
26. A. Asuncion and D. Newman, "UCI machine learning repository," 2007. [Online]. Available: http://www.ics.uci.edu/ mlearn/MLRepository.html
27. E. Müller, S. Günnemann, I. Färber, and T. Seidl, "Discovering multiple clustering solutions: Grouping objects in different views of the data," in ICDM, 2010, p. 1220.
28. E. Bae and J. Bailey, "Coala: A novel approach for the extraction of an alternate clustering of high quality and high dissimilarity," in ICDM, 2006, pp. 53-62.
29. Z. Qi and I. Davidson, "A principled and flexible framework for finding alternative clusterings," in KDD, 2009, pp. 717-726.
30. D. Niu, J. Dy, and M. Jordan, "Multiple Non-Redundant Spectral Clustering Views," in ICML, 2010.