Robust, complete, and efficient correlation clustering

Proceedings of the 7th SIAM International Conference on Data Mining

Volumn , Issue , 2007, Pages 413-418

  Achtert, Elke ^a   Böhm, Christian ^a   Kriegel, Hans Peter ^a   Kröger, Peer ^a   Zimek, Arthur ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CORRELATION METHODS; DATA MINING;

COMPLEX INPUTS; CORRELATION CLUSTERING; DATA POINTS; DATA SPACE; EXPERIMENTAL EVALUATION; PARAMETER SETTING; PARTITION CLUSTERING; ROBUSTNESS AGAINST NOISE;

CLUSTERING ALGORITHMS;

EID: 70449112553     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972771.37     Document Type: Conference Paper

References (16)

1. C. C. Aggarwal, C. M. Procopiuc, J. L. Wolf, P. S. Yu, and J. S. Park. Fast algorithms for projected clustering. In Proc. SIGMOD, 1999.
2. C. C. Aggarwal and P. S. Yu. Finding generalized projected clusters in high dimensional space. In Proc. SIGMOD, 2000.
3. R Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for mining applications. In Proc. SIGMOD, 1998.
4. C. Böhm, K. Kailing, H.-P. Kriegel, and P. Kröger. Density connected clustering with local subspace preferences. In Proc. 1CDM, 2004.
5. C. Böhm, K. Kailing, P. Kröger, and A. Zimek. Computing clusters of correlation connected objects. In Proc. SIGMOD, 2004.
6. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. KDD, 1996.
7. A. Gionis, A. Hinneburg, S. Papadimitriou, and P. Tsapara.s. Dimension induced clustering. In Proc. KDD, 2005.
8. K. Kailing, H.-P. Kriegel, and P. Kröger. Density-connected subspace clustering for high-dimensional data. In Proc. SDM, 2004.
9. J. Liu and W. Wang. OP-Cluster: Clustering by tendency in high dimensional spaces. In Proc. ICDM, 2003.
10. 0. L. Mangasarian and W. H. Wolberg. Cancer diagnosis via linear programming. SIAM News, 23(5):1-18, 1990.
11. J. Pei, X. Zhang, M. Cho, H. Wang, and P. S. Yu. MaPle: A fast algorithm for maximal pattern-based clustering. In Proc. ICDM, 2003.
12. C. M. Procopiuc, M. Jones, P. K. Agarwal, and T. M. Murali. A Monte Carlo algorithm for fast projective clustering. In Proc. SIGMOD, 2002.
13. J. Sander, M. Ester, H.-P. Kriegel, and X. Xu. Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications. Data Mining and Knowledge Discovery, 2:169-194, 1998.
14. A. K. H. Thng, X. Xu, and C. B. Ooi. CURLER: Finding and visualizing nonlinear correlated clusters. In Proc. SIGMOD, 2005.
15. H. Wang, W. Wang, J. Yang, and P. S. Yu. Clustering by pattern similarity in large data sets. In Proc. SIGMOD, 2002.
16. J. Yang, W. Wang, H. Wang, and P. S. Yu. Delta-Clusters: Capturing subspace correlation in a large data set. In Proc. ICDE, 2002.