On k-median clustering in high dimensions

Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms

Volumn , Issue , 2006, Pages 1177-1185

  Chen, Ke ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; COMPUTATIONAL COMPLEXITY; POLYNOMIALS;

EUCLIDEAN SPACES; K-MEDIAN CLUSTERING; METRIC SPACES;

ALGORITHMS;

EID: 33244454316     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1109557.1109687     Document Type: Conference Paper

References (23)

1. P. K. Agarwal, S. Har-Peled, and K. Varadarajan. Geometric approximation via coresets. manuscript, 2004.
2. P. K. Agarwal, S. Har-Peled, and K. R. Varadarajan. Approximating extent measures of points. J. Assoc. Comput. Mach., 51(4):606-635, 2004.
3. S. Arora, P. Raghavan, and S. Rao. Approximation schemes for Euclidean k-median and related problems. In Proc. 30th Annu. ACM Sympos. Theory Comput., pages 106-113, 1998.
4. V. Arya, N. Garg, R. Khandekar, K. Munagala, and V. Pandit. Local search heuristic for k-median and facility location problems. In Proc. 33rd Annu. ACM Sympos. Theory Comput., pages 21-29, 2001.
5. M. Badoiu, S. Har-Peled, and P. Indyk. Approximate clustering via core-sets. In Proc. 34th Annu. ACM Sympos. Theory Comput., pages 250-257, 2002.
6. J. L. Bentley and J. B. Saxe. Decomposable search-ing problems i: Static-to-dynamic transformation. J. Algorithms, 1(4):301-358, 1980.
7. M. Bǎdoiu and K. L. Clarkson. Optimal core-sets for balls. In Proc. 14th ACM-SIAM Sympos. Discrete Algo., pages 801-802, 2003.
8. M. Charikar, S. Guha, E. Tardos, and D. B. Shmoys. A constant-factor approximation algorithm for the k-median problem. In Proc. 31st Annu. ACM Sympos. Theory Comput., pages 1-10, 1999.
9. M. Charikar, L. O'Callaghan, and R. Panigrahy. Better streaming algorithms for clustering problems. In Proc. 35th Annu. ACM Sympos. Theory Comput., pages 30-39, 2003.
10. G. Frahling and C. Sohler. Coresets in dynamic geometric data streams. In Proc. 37th Annu. ACM Sympos. Theory Comput., pages 209-217, 2005.
11. T. Gonzalez. Clustering to minimize the maximum intercluster distance. Theoret. Comput. Sci., 38:293-306, 1985.
12. S. Guha, N. Mishra, R. Motwani, and L. O'Callaghan. Clustering data streams. In Proc. 41th Annu. IEEE Sympos. Found. Comput. Sci., pages 359-366, 2000.
13. S. Har-Peled and A. Kushal. Smaller coresets for k-median and k-means clustering. In Proc. 21th Annu. ACM Sympos. Comput. Geom., pages 126-134, 2005.
14. S. Har-Peled and S. Mazumdar. Coresets for kmeans and k-median clustering and their applications. In Proc. 36th Annu. ACM Sympos. Theory Comput., pages 291-300, 2004.
15. D. Haussler. Decision theoretic generalizations of the pac model for neural net and other learning applications. Inf. Comput., 100(1):78-150, 1992.
16. P. Indyk. Sublinear time algorithms for metric space problems. In Proc. 31st Annu. ACM Sympos. Theory Comput., pages 154-159, 1999.
17. P. Indyk. Algorithms for dynamic geometric problems over data streams. In Proc. 36th Annu. ACM Sympos. Theory Comput, pages 373-380, 2004.
18. K. Jain and V. V. Vazirani. Primal-dual approximation algorithms for metric facility location and kmedian problems. In Proc. 40th Annu. IEEE Sympos. Found. Comput. Sci., pages 2-13, 1999.
19. S. G. Kolliopoulos and S. Rao. A nearly linear-time approximation scheme for the euclidean k-median problem. In Proc. 7th Annu. Euro. Sympos. Algorithms, pages 378-389, 1999.
20. A. Kumar, Y. Sabharwal, and S. Sen. A simple linear time (1 + ε)-approximation algorithm for kmeans clustering in any dimensions. In Proc. 45nd Annu. IEEE Sympos. Found. Comput. Sci., pages 454-462, 2004.
21. A. Kumar, Y. Sabharwal, and S. Sen. Linear time algorithms for clustering problems in any dimensions. In Proc. 32nd Int. Colloq. Automata Lang. Prog., pages 1374-1385, 2005.
22. R. Mettu and C. G. Plaxton. Optimal time bounds for approximate clustering. In Proc. 18th Conf. Uncertainty Artif. Intell., pages 344-351, 2002.
23. N. Mishra, D. Oblinger, and L. Pitt. Sublinear time approximate clustering. In Proc. 12th ACM-SIAM Sympos. Discrete Algo., pages 439-447, 2001.