OP-cluster: Clustering by tendency in high dimensional space

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2003, Pages 187-194

  Liu, Jinze ^a   Wang, Wei ^a  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 78149310670     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. J. Ayres, J. E. Gehrke, T. Yiu, and J. Flannick. Sequential PAttern Mining Using Bitmaps. In SIGKDD, July 2002.
2. R. C. Agarwal, C. C. Aggarwal, and V. Parsad. Depth first generation of long patterns. In SIGKDD, 2000.
3. C. C. Aggarwal, C. Procopiuc, J. Wolf, P. S. Yu, and J. S. Park. Fast algorithms for projected clustering. In SIGMOD, 1999.
4. C. C. Aggarwal and P. S. Yu. Finding generalized projected clusters in high dimensional spaces. In SIGMOD, pages 70-81, 2000.
5. R. Agrawal and R. Srikant. Mining sequential patterns. In ICDE, 3-14, Mar. 1995.
6. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Authomatic subspace clustering of high dimensional data for data mining applications. In SIGMOD, 1998.
7. A. Ben-Dor, B. Chor, R.Karp, and ZYakhini. Discovering Local Structure in Gene Expression Data: The Order-Preserving Submatrix Problem. In RECOMB 2002.
8. 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.
9. C. H. Cheng, A. W Fu, and Y. Zhang. Entropy-based subspace clustering for mining numerical data. In SIGKDD, pages 84-93, 1999.
10. Y Cheng and G. Church. Biclustering of expression data. In Proc. of 8th International Conference on Intelligent System for Molecular Biology, 2000.
11. 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.
12. H.V.Jagadish, J.Madar, and R. Ng. Semantic compression and pttern extraction with fasicicles. In VLDB, pages 186-196, 1999.
13. J.Liu and W.Wang. Flexible clustering by tendency in high dimensional spaces. Technical Report TR03-009, Computer Science Department, UNC-CH, 2003.
14. J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen U. Dayal, and M.-C. Hsu. PrefixSpan mining sequential patterns e.ciently by prefix projected pattern growth. In ICDE 2001, pages 215-226, Apr. 2001.
15. R.Srikant and RAgrawal. Mining sequential patterns: Generalizations and performance improvements. In EDBT'96, pages 3-17, Mar. 1996.
16. 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.
17. H. Wang, W. Wang, J. Yang, and P. Yu. Clustering by pattern similarity in large data sets, in SIGMOD, pp. 394-405, 2002.
18. J. Yang, W. Wang, H. Wang, and P. S. Yu. δ-clusters: Capturing subspace correlation in a large data set. In ICDE, pages 517-528, 2002.
19. J. Yang, W. Wang, P. Yu, and J. Han. Mining long sequential patterns in a noisy environment. In SIGMOD,pp.406-417, 2002.
20. T. Zhang, R. Ramakrishnan, and M. Livny. Birch: An efficient data clustering method for very large databases. In SIGMOD, pages 103114, 1996.
21. M. J. Zaki, S. Parthasarathy, M.Orihara, and W. Li. Parallel algorithm for discovery of association rules. In DMKD, 343-374, 1997.