COALA: A novel approach for the extraction of an alternate clustering of high quality and high dissimilarity

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2006, Pages 53-62

  Bae, Eric ^a   Bailey, James ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COALA; REAL DATASETS; SINGLE SOLUTION; SYNTHETIC DATASETS;

ALGORITHMS; DATA MINING; DATA STRUCTURES; LEARNING SYSTEMS; USER INTERFACES;

CLUSTER ANALYSIS;

EID: 84873117260     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2006.37     Document Type: Conference Paper

References (26)

1. J. Bezdek and N. Pal. Some new indexes of cluster validity. Sys., Man and Cybernetics, 28(3):301-315, 1998.
2. J. Bezdek, L. Wanquing, Y. Attikiouzel, and M. Windham. A geometric approach to cluster validity for normal mixtures. Soft Computing, pages 166-179, 1997.
3. G. Chechik and N. Tishby. Extracting relevant structures with side information. In Advances in Neural Info. Processing Systems, pages 857-864. MIT Press, 2003.
4. K. Chen and L. Liu. The "best k" for entropy-based categorical data clustering. In Inter. Conf. on Seien, and Stat. Database Management, pages 253-262, 2005.
5. I. Davidson and S. Ravi. Clustering with constraints: Feasibility issues and the k-means algorithm. In SIAM Intern. Conf. on Data Mining, 2005.
6. I. Davidson and S. Ravi. Identifying and generating easy sets of constraints for clustering. In AAAI Conference, 2006.
7. J. Dunn. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. In Journal of Cybernetics, pages 32-57, 1974.
8. S. Eissen and B. Stein. Analysis of clustering algorithms for web-based search. In International Conference on Practical Aspects of Knowledge Management, pages 168-178, 2002.
9. D. Eppstein. Fast hierarchical clustering and other applications of dynamic closest pairs. In SODA: ACM-SIAM Symposium on Discrete Algorithms, page 1, 1998.
10. A. Fred. Finding consistent clusters in data partitions. In Multiple Classifier Systems, pages 309-318, 2001.
11. D. Gondek and T. Hofmann. Conditional information bottleneck clustering. Intern. Conf. on Data Mining, 2003.
12. D. Gondek and T. Hofmann. Non-redundant clustering with conditional ensembles. International Conference on Knowledge Discovery and Data Mining, pages 70-77, 2005.
13. D. Gondek, S. Vaithyanathan, and A. Garg. Clustering with model-level constraints. In SIAM International Conference on Data Mining, 2005.
14. S. Guha, R. Rastogi, and K. Shim. ROCK: A robust clustering algorithm for categorical attributes. Information Systems, 25:345-366, 2000.
15. B. Larsen and C. Aone. Fast and effective text mining using linear-time document clustering. In Intern. Conf. on Knowledge Discovery and Data Mining, pages 16-22, 1999.
16. M. Law, A. Topchy, and A. Jain. Multiobjective data clustering. In Computer Society Conference on Computer Vision and Pattern Recognition, pages 424-430, 2004.
17. M. Nanni. Speeding-up hierarchical agglomerative clustering in presence of expensive metrics. In PAKDD, pages 378-387, 2005.
18. L. Parsons, E. Haque, and H. Liu. Subspace clustering for high dimensional data: a review. SIGKDD Explor. Newsl., 6(1):90-105, 2004.
19. A. Patrikainen. Methods for comparing subspace clusterings, master's thesis, www.cis.hut.fi/ annep/lisuri.pdf, 2005.
20. S. Rajasekaran. Efficient parallel hierarchical clustering algorithms. Parallel Distrib. Syst., 16(6):497-502, 2005.
21. N. Tishby, F. Pereira, and W. Bialek. The information bottleneck method. Allerton Conference on Communication, Control and Computing, pages 368-377, 1999.
22. A. Topchy, H. Martin, C. Law, A. Jain, and A. Fred. Analysis of consensus partition in cluster ensemble. In Intern. Conf. on Data Mining, pages 225-232, 2004.
23. A. Voorhees. Implementing agglomerative hierarchical clustering algorithms for use in document retrieval. Info. Processing and Management, pages 465-476, 1986.
24. K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl. Constrained k-means clustering with background knowledge. In Intern. Conf. on Machine Learning, pages 577-584, 2001.
25. E. Xing, A. Ng, M. Jordan, and S. Russell. Distance metric learning, with application to clustering with sideinformation. Advances in NIPS, 15:505-512, 2002.
26. D. Zhou, J. Li, and H. Zha. A new mallows distance based metric for comparing clusterings. International Conference on Machine Learning, pages 1028-1035, 2005.