Subspace correlation clustering: Finding locally correlated dimensions in subspace projections of the data

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2012, Pages 352-360

  Günnemann, Stephan ^a   Färber, Ines ^a   Virochsiri, Kittipat ^a   Seidl, Thomas ^a  

^a RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

linear correlations; overlapping clusters

Indexed keywords

COMPLEX DATA; CORRELATION ANALYSIS; CORRELATION CLUSTERING; LINEAR CORRELATION; OVERLAPPING CLUSTERS; OVERLAPPING PATTERNS; PRUNING TECHNIQUES; SUBSPACE CLUSTERING; SUBSPACE PROJECTION; TRADITIONAL CLUSTERING;

CLUSTERING ALGORITHMS;

DATA MINING;

EID: 84866030149     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2339530.2339588     Document Type: Conference Paper

References (22)

1. E. Achtert, C. Böhm, J. David, P. Kröger, and A. Zimek. Global correlation clustering based on the hough transform. SADM, 1(3):111-127, 2008.
2. E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, and A. Zimek. Deriving quantitative models for correlation clusters. In KDD, pages 4-13, 2006.
3. E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, and A. Zimek. On exploring complex relationships of correlation clusters. In SSDBM, page 7, 2007.
4. E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, and A. Zimek. Robust, complete, and efficient correlation clustering. In SDM, 2007.
5. C. C. Aggarwal and P. S. Yu. Finding generalized projected clusters in high dimensional spaces. In SIGMOD, pages 70-81, 2000.
6. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD, pages 94-105, 1998.
7. I. Assent, R. Krieger, E. Müller, and T. Seidl. Inscy: Indexing subspace clusters with in-process-removal of redundancy. In ICDM, pages 719-724, 2008.
8. M. S. Aziz and C. K. Reddy. A robust seedless algorithm for correlation clustering. In PAKDD (1), pages 28-37, 2010.
9. N. Bansal, A. Blum, and S. Chawla. Correlation clustering. Machine Learning, 56(1-3):89-113, 2004.
10. K. S. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is nearest neighbor meaningful? In ICDT, pages 217-235, 1999.
11. C. Böhm, K. Kailing, P. Kröger, and A. Zimek. Computing clusters of correlation connected objects. In SIGMOD, pages 455-466, 2004.
12. J. Cohen, P. Cohen, S. G. West, and L. S. Aiken. Applied multiple regression/correlation analysis for the behavioral sciences. Routledge, 3rd edition, 2002.
13. R. Freund, W. Wilson, and P. Sa. Regression analysis: statistical modeling of a response variable. Academic Press, 2006.
14. S. Günnemann, E. Müller, I. Färber, and T. Seidl. Detection of orthogonal concepts in subspaces of high dimensional data. In CIKM, pages 1317-1326, 2009.
15. R. Harpaz and R. M. Haralick. Mining subspace correlations. In CIDM, pages 335-342, 2007.
16. H.-P. Kriegel, P. Kröger, and A. Zimek. Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. TKDD, 3(1), 2009.
17. P. Kröger, H.-P. Kriegel, and K. Kailing. Density-connected subspace clustering for high-dimensional data. In SDM, pages 246-257, 2004.
18. D. Niu, J. G. Dy, and M. I. Jordan. Multiple non-redundant spectral clustering views. In ICML, pages 831-838, 2010.
19. A. Patrikainen and M. Meila. Comparing subspace clusterings. TKDE, 18(7):902-916, 2006.
20. J. Yang, W. Wang, H. Wang, and P. S. Yu. delta-clusters: Capturing subspace correlation in a large data set. In ICDE, pages 517-528, 2002.
21. X. Zhang, F. Pan, and W. Wang. Care: Finding local linear correlations in high dimensional data. In ICDE, pages 130-139, 2008.
22. X. Zhang, F. Pan, and W. Wang. Redus: finding reducible subspaces in high dimensional data. In CIKM, pages 961-970, 2008.