Robust clustering in arbitrarily oriented subspaces

Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130

Volumn 2, Issue , 2008, Pages 763-774

  Achtert, Elke ^a   Böhm, Christian ^a   David, Jörn ^a   Kröger, Peer ^a   Zimek, Arthur ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING;

DATA SPACE; DATABASE OBJECTS; EXPERIMENTAL EVALUATION; NOISY ENVIRONMENT; PARAMETER SPACES; ROBUST CLUSTERING; SUBSPACE CLUSTERS;

CLUSTERING ALGORITHMS;

EID: 52649169009     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972788.69     Document Type: Conference Paper

References (19)

1. E. Achtert, C. Böhm, H.-P. Kriegel, P. Kroger, and A. Zimek. On exploring complex relationships of correlation clusters. In Proceedings of the 19th International Conference on Scientific and Statistical Database Management (SSDBM), Banff, Canada, 2007.
2. E. Achtert, C. Böhm, H.-P. Kriegel, P. Kroger, and A. Zimek. Robust, complete, and efficient correlation clustering. In Proceedings of the 7th SIAM Interna tional Conference on Data Mining (SDM), Minneapolis, MN, 2007.
3. C. C. Aggarwal, C. M. Procopiuc, J. L. Wolf, P. S. Yu, and J. S. Park. Fast algorithms for projected clustering. In Proceedings of the ACM International Conference on Management of Data (SIGMOD), Philadelphia, PA, 1999.
4. C. C. Aggarwal and P. S. Yu. Finding generalized projected clusters in high dimensional space. In Proceedings of the ACM International Conference on Management of Data (SIGMOD), Dallas, TX, 2000.
5. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In Proceedings of the ACM International Conference on Management of Data (SIGMOD), Seattle, WA, 1998.
6. C. Böhm, K. Kailing, H.-P. Kriegel, and P. Kröger. Density connected clustering with local subspace preferences. In Proceedings of the 4th International Conference on Data Mining (ICDM), Brighton, U.K., 2004.
7. C. Böhm, K. Kailing, P. Kröger, and A. Zimek. Computing clusters of correlation connected objects. In Proceedings of the ACM International Conference on Management of Data (SIGMOD), Paris, France, 2004.
8. K. Chakrabarti and S. Mehrotra. Local dimensionality reduction: A new approach to indexing high dimensional spaces. In Proceedings of the 26th International Conference on Very Large Data Bases (VLDB), Cairo, Egypt, 2000.
9. R. O. Duda and P. E. Hart. Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM, 15(1):11-15, 1972.
10. A. Hinneburg, C. C. Aggarwal, and D. A. Keim. What is the nearest neighbor in high dimensional spaces? In Proceedings of the 26th International Conference on Very Large Data Bases (VLDB), Cairo, Egypt, 2000.
11. P. V. C. Hough. Methods and means for recognizing complex patterns. U.S. Patent 3069654, December 18 1962.
12. K. Kailing, H.-P. Kriegel, and P. Kroger. Density- connected subspace clustering for high-dimensional data. In Proceedings of the 4th SIAM International Conference on Data Mining (SDM), Orlando, FL, 2004.
13. J. Liu and W. Wang. OP-Cluster: Clustering by tendency in high dimensional spaces. In Proceedings of the 3th International Conference on Data Mining (ICDM), Melbourne, FL, 2003.
14. J. Pei, X. Zhang, M. Cho, H. Wang, and P. S. Yu. MaPle: A fast algorithm for maximal pattern-based clustering. In Proceedings of the 3th International Conference on Data Mining (ICDM), Melbourne, FL, 2003.
15. C. M. Procopiuc, M. Jones, P. K. Agarwal, and T. M. Murali. A Monte Carlo algorithm for fast projective clustering. In Proceedings of the ACM International Conference on Management of Data (SIGMOD), Madison, WI, 2002.
16. P. T. Spellman, G. Sherlock, M. Q. Zhang, V. R. Iyer, K. Anders, M. B. Eisen, P. O. Brown, D. Botstein, and B. Futcher. Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell, 9:3273-3297, 1998.
17. A. K. H. Tung, X. Xu, and C. B. Ooi. CURLER: Finding and visualizing nonlinear correlated clusters. In Proceedings of the ACM International Conference on Management of Data (SIGMOD), Baltimore, ML, 2005.
18. H. Wang, W. Wang, J. Yang, and P. S. Yu. Clustering by pattern similarity in large data sets. In Proceedings of the ACM International Conference on Management of Data (SIGMOD), Madison, WI, 2002.
19. J. Yang, W. Wang, H. Wang, and P. S. Yu. δ-clusters: Capturing subspace correlation in a large data set. In Proceedings of the 18th International Conference on Data Engineering (ICDE), San Jose, CA, 2002.