A greedy search approach to co-clustering sparse binary matrices

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn , Issue , 2006, Pages 363-370

  Angiulli, Fabrizio ^a   Cesario, Eugenio ^a   Pizzuti, Clara ^a  

^a ICAR CNR   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CODES; DATA ACQUISITION; MATRIX ALGEBRA;

GREEDY SEARCH APPROACH; SPARSE BINARY MATRICES;

CLUSTERING ALGORITHMS;

EID: 38949211820     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2006.10     Document Type: Conference Paper

References (21)

1. C. C. Aggarwal, C. P. J. L. Wolf, P. S. Yu, and J. S. ParkG. Fast algorithms for projected clustering. In Proceedings of the ACM International Conference On Management of Data (SIGMOD'99), pages 61-72, 1999.
2. C. C. Aggarwal and P. S. Yu. Finding generalized projected clusters in high dimensional spaces. In Proceedings of the ACM International Conference On Management of Data (SIGMOD'OO), pages 70-81, 2000.
3. R. Agrawal, J. G. 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'98), pages 94-105, 1998.
4. R. Agrawal and R. Skikart. Fast algorithms for mining association rules in large datasets. In Proceedings of the 20th Very Large Database Conference (VLDB'94), pages 487-499, 1994.
5. F. Angiulli and C. Pizzuti. Gene expression biclustering using random walk strategies. In Proceedings of the 7th International Conference on Data Warehousing and Knowledge Discovery (DAWAK 2005), pages 509-519, 2005.
6. Y. Cheng and G. M. Church. Biclustering of expression data. In Proceedings of the 8th International Conference On Intelligent Systems for Molecular Biology (ISMB'00), pages 93-103, 2000.
7. H. Cho, I. S. Dhillon, Y. Guan, and S. Sra. Minimum sum-squared residue co-clustering of gene expression data. In Proceedings of the Fourth SIAM International Conference on Data Mining (SDM'04), 2004.
8. S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Harshman. Indexing by latent semantic analysis. JASI, 41:391-407, 1990.
9. I. S. Dhillon. Co-clustering documents and words using bipartite spectral graph partitioning. In Proceedings of the Seventh International ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'01), pages 269-274, 2003.
10. I. S. Dhillon, S. Mallela, and D. Modha. Informationtheoretic co-clustering. In Proceedings of the Ninth International ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'03), pages 89-98, 2003.
11. A. Gionis, H. Mannila, and J. Seppanen. Geometric and combinatorial tiles in 0-1 data. In Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'04), pages 173-184, 2004.
12. J. Han, J. Pei, and R. Mao. Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Discovery, 8(1):53-87, 2004.
13. J. A. Hartigan. Direct clustering of a data matrix. Journal of the American Statistical Association, 67(337): 123-129, 1972.
14. R. D. A. JainM. Algorithms for Clustering Data. Prentice Hall, 1988.
15. G. Karypis and V. Kumar. Multi-level algorithms for multi-constraints graph partitioning. In Proceedings of the SC conference, pages 1-13, 1998.
16. L. Kaufman and P. Rousseew. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, 1990.
17. S. Lonardi, W. Szpankowski, and Q. Yang. Finding biclusters by random projections. In Proceedings of the 15th Annual Symposium on Combinatorial Pattern Matching, (CPM '04), pages 102-116, 2004.
18. B. Long, Z. Zhang, and P. Yu. Co-clustering by block value decomposition. In Proceedings of the Eleventh International ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'05), pages 635-640, 2005.
19. S. C. Madeira and A. L. Oliveira. Biclustering algorithms for biological data analysis: A survey. IEEE Transactions on Computational Biology and Bioinformatics, 1(1):24-45, 2004.
20. H. Nagesh, S. Goil, and A. Choundhary. Mafia: Efficient and scalable subspace clustering for very large datasets. In Technical report 9906-010, Northwestern University, 1999.
21. J. Yang, W. Wang, H. Wang, and P. Yu. Enhanced biclustering on expression data. In Proceedings of the 3rd IEEE Conference On Bioinformatics and Bioengineering (BIBE'03), pages 321-327, 2003.