Clustering by pattern similarity in large data sets

Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, SIGMOD 2002

Volumn , Issue , 2002, Pages 394-405

  Wang, Haixun ^a   Wang, Wei ^a   Yang, Jiong ^a   Yu, Philip S ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; GENE EXPRESSION;

CLUSTERING MODEL; CLUSTERINGS; DNA MICROARRAY ANALYSIS; E-COMMERCE APPLICATIONS; ENVIRONMENTAL STIMULI; EUCLIDEAN DISTANCE; EXPRESSION LEVELS; GENE REGULATORY NETWORKS; LARGE DATASETS; PATTERN SIMILARITY;

COLLABORATIVE FILTERING;

EID: 85085674656     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/564691.564737     Document Type: Conference Paper

References (21)

1. R. C. Agarwal, C. C. Aggarwal, and V. Parsad. Depth first generation of long patterns. In SIGKDD, 2000.
2. C. C. Aggarwal, C. Procopiuc, J. Wolf, P. S. Yu, and J. S. Park. Fast algorithms for projected clustering. In SIGMOD, 1999.
3. C. C. Aggarwal and P. S. Yu. Finding generalized projected clusters in high dimensional spaces. In SIGMOD, pages 70-81, 2000.
4. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Authomatic subspace clustering of high dimensional data for data mining applications. In SIGMOD, 1998.
5. K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is nearest neighbors meaningful. In Proc. of the Int. Conf. Database Theories, pages 217-235, 1999.
6. C. H. Cheng, A. W. Fu, and Y. Zhang. Entropy-based subspace clustering for mining numerical data. In SIGKDD, pages 84-93, 1999.
7. Y. Cheng and G. Church. Biclustering of expression data. In Prec. of 8th International Conference on Intelligent System for Molecular Biology, 2000.
8. P. D'haeseleer, S. Liang, and R. Somogyi. Gene expression analysis and genetic network modeling. In Pacific Symposium on Biocomputing, 1999.
9. M. Ester, H. Kriegel, J. Sander, and X. Xu. A density-bsed algorithm for discovering clusters in large spatial databases with noise. In SIGKDD, pages 226-231, 1996.
10. D. H. Fisher. Knowledge acquisition via incremental conceptual clustering. In Machine Learning, 1987.
11. K. Fukunaga. Introduction to Statistical Pattern Recognition. Academic Press, 1990.
12. H. V. Jagadish, J. Madar, and R. Ng. Semantic compression and pattern extraction with fascicles. In VLDB, pages 186-196, 1999.
13. R. S. Michalski and R. E. Stepp. Learning from observation: conceptual clustering. In Machine Learning: An Artificial Intelligence Approach, pages 331-363, 1983.
14. F. Murtagh. A survey of recent hierarchical clustering algorithms. In The Computer Journal, 1983.
15. H. Nagesh, S. Goil, and A. Choudhary. Mafia: Efficient and scalable subspace clustering for very large data sets. Technical Report 9906-010, Northwestern University, 1999.
16. R. T. Ng and J. Han. Efficient and effective clustering methods for spatial data mining. In VLDB, 1994.
17. J. Riedl and J. Konstan. Movielens dataset. In http://www.cs. umn.edu/Research/Group Lens.
18. U. Shardanand and P. Maes. Social information filtering: Algorithms for automating 'word of mouth'. In Proceeding of ACM CHI, pages 210-217, 1995.
19. S. Tavazoie, J. Hughes, M. Campbell, R. Cho, and G. Church. Yeast micro data set. In http://arep. med. harvard, edu/biclustering/yeast. matrix, 2000.
20. J. Yang, W. Wang, H. Wang, and P. S. Yu. 6-clusters: Capturing subspace correlation in a large data set. In ICDE, pages 517-528, 2002.
21. T. Zhang, R. Ramakrishnan, and M. Livny. Birch: An efficient data clustering method for very large databases. In SIGMOD, pages 103-114, 1996.