Constrained locally weighted clustering

Proceedings of the VLDB Endowment

Volumn 1, Issue 1, 2008, Pages 90-101

  Cheng, Hao ^a   Hua, Kien A ^a   Vu, Khanh ^a  

^a University of Central Florida   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE COMBINATIONS; CLUSTERING ACCURACY; CONVENTIONAL METHODS; LOCAL CORRELATIONS; MULTIDIMENSIONAL DATA; SQUARED DISTANCES; STATE-OF-THE-ART ALGORITHMS; WEIGHTING VECTOR;

CLUSTERING ALGORITHMS;

EID: 77952760641     PISSN: None     EISSN: 21508097     Source Type: Conference Proceeding    
DOI: 10.14778/1453856.1453871     Document Type: Article

References (37)

1. http://www.cs.utexas.edu/users/ml/risc/code/.
2. http://www.ews.uiuc.edu/~dengcai2/data/data.html.
3. C. C. Aggarwal, J. L. Wolf, P. S. Yu, C. Procopiuc, and J. S. Park. Fast algorithms for projected clustering. In SIGMOD '99: Proceedings of the 1999 ACM SIGMOD international conference on Management of data, pages 61-72, 1999.
4. C. C. Aggarwal and P. S. Yu. Finding generalized projected clusters in high dimensional spaces. SIGMOD Rec., 29(2):70-81, 2000.
5. M. Al-Razgan and C. Domeniconi. Weighted clustering ensembles. In SDM '06: Proceedings of the Fourth SIAM International Conference on Data Mining, 2006.
6. A. Bar-Hillel, N. S. Tomer Hertz, and D. Weinshall. Learning distance functions using equivalence relations. In ICML '03: Proceedings of the Twentith International Conference on Machine Learning, 2003.
7. S. Basu, A. Banerjee, and R. J. Mooney. Semi-supervised clustering by seeding. In ICML '02: Proceedings of the Nineteenth International Conference on Machine Learning, pages 27-34, 2002.
8. M. Bilenko, S. Basu, and R. J. Mooney. Integrating constraints and metric learning in semi-supervised clustering. In ICML '04: Proceedings of the twenty-first international conference on Machine learning, page 11, 2004.
9. H. Cheng, K. A. Hua, and K. Vu. Leveraging user query log: Toward improving image data clustering. In CIVR '08: ACM International Conference on Image and Video Retrieval, 2008.
10. I. Davidson and S. S. Ravi. Clustering with constraints: Feasibility issues and the k-means algorithm. In SDM '05: Proceedings of the Fourth SIAM International Conference on Data Mining, 2005.
11. I. Davidson and S. S. Ravi. Identifying and generating easy sets of constraints for clustering. In AAAI '06: Proceedings, The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, 2006.
12. I. Davidson, K. Wagstaff, and S. Basu. Measuring constraint-set utility for partitional clustering algorithms. In Proceeding of ECML/PKDD, 2006.
13. C. Ding and X. He. K-means clustering via principal component analysis. In ICML '04: Proceedings of the twenty-first international conference on Machine learning, page 29, New York, NY, USA, 2004. ACM Press.
14. C. Domeniconi, D. Papadopoulos, D. Gunopulos, and S. Ma. Subspace clustering of high dimensional data. In SDM '04: Proceedings of the Fourth SIAM International Conference on Data Mining, 2004.
15. C. Elkan. Using the triangle inequality to accelerate k-means. In Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), pages 147-153, 2003.
16. M. Halkidi, D. Gunopulos, M. Vazirgiannis, N. Kumar, and C. Domeniconi. A clustering framework based on subjective and objective validity criteria. ACM Trans. Knowl. Discov. Data, 1(4):1-25, 2008.
17. G. Hamerly and C. Elkan. Alternatives to the k-means algorithm that find better clusterings. In CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management, pages 600-607, New York, NY, USA, 2002. ACM Press.
18. A. K. Jain and R. C. Dubes. Algorithms for clustering data. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1988.
19. A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: a review. ACM Comput. Surv., 31(3):264-323, 1999.
20. E. T. Jaynes. Probability Theory: The Logic of Science. Cambridge University Press, 2003.
21. S. D. Kamvar, D. Klein, and C. D. Manning. Interpreting and extending classical agglomerative clustering algorithms using a model-based approach. In ICML '02: Proceedings of the Nineteenth International Conference on Machine Learning, pages 283-290, San Francisco, CA, USA, 2002. Morgan Kaufmann Publishers Inc.
22. D. Klein, S. D. Kamvar, and C. D. Manning. From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In ICML '02: Proceedings of the Nineteenth International Conference on Machine Learning, pages 307-314, 2002.
23. D. Newman, S. Hettich, C. Blake, and C. Merz. UCI repository of machine learning databases, 1998.
24. A. Y. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In NIPS '02: Advances in Neural Information Processing Systems, 2002.
25. L. Parsons, E. Haque, and H. Liu. Subspace clustering for high dimensional data: a review. SIGKDD Explor. Newsl., 6(1):90-105, 2004.
26. W. M. Rand. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66:622-626, 1971.
27. K. Vu, K. A. Hua, H. Cheng, and S.-D. Lang. A non-linear dimensionality-reduction technique for fast similarity search in large databases. In SIGMOD '06: Proceedings of the 2006 ACM SIGMOD international conference on Management of data, pages 527-538, New York, NY, USA, 2006. ACM.
28. K. Wagstaff, S. Basu, and I. Davidson. When is constrained clustering beneficial, and why?. In AAAI '06: Proceedings, The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, 2006.
29. K. Wagstaff and C. Cardie. Clustering with instance-level constraints. In ICML '00: Proceedings of the Seventeenth International Conference on Machine Learning, pages 1103-1110, San Francisco, CA, USA, 2000. Morgan Kaufmann Publishers Inc.
30. K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl. Constrained k-means clustering with background knowledge. In ICML '01: Proceedings of the Eighteenth International Conference on Machine Learning, pages 577-584, San Francisco, CA, USA, 2001. Morgan Kaufmann Publishers Inc.
31. T. Wang, Y. Rui, and S.-M. Hu. Optimal adaptive learning for image retrieval. In CVPR '01: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1140-1147, 2001.
32. E. Xing, A. Y. Ng, M. Jordan, and S. Russell. Distance metric learning, with application to clustering with side-information. In NIPS '03: Advances in Neural Information Processing Systems, 2003.
33. H. Xiong, J. Wu, and J. Chen. K-means clustering versus validation measures: a data distribution perspective. In KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 779-784, 2006.
34. D. S. Yeung and X. Z. Wang. Improving performance of similarity-based clustering by feature weight learning. IEEE Trans. Pattern Anal. Mach. Intell., 24(4):556-561, 2002.
35. Y. Zhao and G. Karypis. Criterion functions for document clustering: Experiments and analysis. Technical Report CS 01-40, Department of Computer Science, University of Minnesota, 2001.
36. Y. Zhao and G. Karypis. Evaluation of hierarchical clustering algorithms for document datasets. In CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management, pages 515-524, New York, NY, USA, 2002. ACM Press.
37. X. Zheng, D. Cai, X. He, W.-Y. Ma, and X. Lin. Locality preserving clustering for image database. In MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia, pages 885-891, New York, NY, USA, 2004. ACM Press.