CRD: Fast co-clustering on large datasets utilizing sampling-based matrix decomposition

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2008, Pages 173-184

  Pan, Feng ^a   Zhang, Xiang ^a   Wang, Wei ^a  

^a Brooks Computer Science Building   (United States)

Author keywords

Co clustering; Matrix decomposition

Indexed keywords

CLUSTER ANALYSIS; COBALT; COBALT COMPOUNDS; DATA STRUCTURES; DECISION SUPPORT SYSTEMS; FLOW OF SOLIDS; INFORMATION MANAGEMENT;

CLUSTERING STRUCTURES; CO-CLUSTERING; COMPUTATIONAL COSTS; DATA MATRIXES; DATA SETS; EXECUTION TIMES; GENERAL FRAMEWORKS; LARGE DATA SETS; MAIN MEMORIES; MATRIX DECOMPOSITION; MATRIX DECOMPOSITIONS; RECOMMENDATION SYSTEMS; SYNTHETIC DATUMS; TEXT DATA MININGS;

CLUSTERING ALGORITHMS;

EID: 57149147732     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1376616.1376637     Document Type: Conference Paper

References (20)

1. A. Banerjee, I. Dhillon, J. Ghosh, S. Merugu, and D. S. Modha. A generalized maximum entropy approach to bregman co-clustering and matrix approximation. In KDD, 2004.
2. D. Chakrabarti, S. Papadimitriou, D. Modha, and C. Faloutsos. Fully automatic cross-associations. In ACM SIGKDD'04 Conference Proceedings, 2004.
3. Y. Cheng and G. Church. Biclustering of experssion data. In Proc. of the Eighth Int. Conf. on Intelligent Systems for Molecular Biology, 2000.
4. S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41 (6):391-407, 1990.
5. I. S. Dhillon, S. Mallela, and D. S. Modha. Information-theoretic co-clustering. In KDD, 2003.
6. C. Ding, X. He, and H. Simon. On the equivalence of nonnegative matrix factorization and. spectral clustering. In SDM, 2005.
7. C. Ding, X. He, H. Zha, M. Gu, and H. Simon. A min-max cut algorithm for graph partitioning and data clustering. In ICDM, 2001.
8. C. Ding, T. Li, W. Peng, and H. Park. Orthogonal nonnegative matrix tri-factorizations for clustering. In KDD, 2006.
9. P. Drineas, R. Kannan, and M. W. Mahoney. Fast monte carlo algorithms for matrices iii: Computing a compressed approximate matrix decomposition. In SIAM Journal of Computing, 2005.
10. P. Drineas, I. Kerenidis, and P. Raghavan. Competitive recommendation systems. In STOC, 2002.
11. G. Golub and A. Loan. Matrix computations. Johns Hopkins University Press, Baltimore, Maryland, 1996.
12. J. A. Hartigan. Direct clustering of a data matrix. Journal of the American Statistical Association, 67(337): 123-129, 1972.
13. D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401:788-791, 1999.
14. T. Li. A general model for clustering binary data. In KDD, 2005.
15. B. Long, Z. Zhang, and P. Yu. Co-clustering by block value decomposition. In KDD, 2005.
16. G. Natsoulis and et. al. Classification of a large microarray data set: Algorithm comparison and analysis of drug signatures. Genome Res., 15:724-736, 2005.
17. N. Slonim and N. Tishby. Document clustering using word clusters via the information bottleneck method. In SIGIR, 2000.
18. J. Sun, Y. Xie, H. Zhang, and C. Faloutsos. Less is more: Compact matrix decomposition for large sparse graphs. In SDM, 2007.
19. W. Xu, X. Liu, and Y. Gong. Document clustering based on non-negative matrix factorization. In SIGIR, 2003.
20. X. Xu, Y. Lu, A. Tung, and W. Wang, Mining shifting-and-scaling co-regulation patterns on gene expression profiles. In ICDE, 2006.