Approximation algorithms for clustering uncertain data

Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems

Volumn , Issue , 2008, Pages 191-199

  Cormode, Graham ^a   McGregor, Andrew ^b  

^a AT AND T LABS RESEARCH   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Clustering; Probabilistic data

Indexed keywords

BICRITERIA APPROXIMATIONS; CLUSTERING; CONSTANT FACTORS; DATA ANALYSIS; K CENTERS; K-MEANS; MINING ALGORITHMS; MINING PROBLEMS; NATURAL GENERALIZATIONS; OPTIMAL CENTERS; PROBABILISTIC DATA; PROBABILITY DENSITIES; RECORD LINKAGES; SENSOR NETWORK MEASUREMENTS; UNCERTAIN DATUMS;

CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; DATA MINING; DATA PROCESSING; DATABASE SYSTEMS; DECISION SUPPORT SYSTEMS; FLOW OF SOLIDS; INFORMATION MANAGEMENT; POLYNOMIAL APPROXIMATION; PROBABILITY; PROBABILITY DENSITY FUNCTION; SENSOR NETWORKS; UNCERTAINTY ANALYSIS;

APPROXIMATION ALGORITHMS;

EID: 57349191578     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1376916.1376944     Document Type: Conference Paper

References (28)

1. C. Aggarwal and P. S. Yu. Framework for clustering uncertain data streams. In IEEE International Conference on Data Engineering, 2008.
2. D. Arthur and S. Vassilvitskii. kmeans++: The advantages of careful seeding. In ACM-SIAM Symposium on Discrete Algorithms, pages 1027-1035, 2007.
3. V. Arya, N. Garg, R. Khandekar, A. Meyerson, K. Munagala, and V. Pandit. Local search heuristics for k-median and facility location problems. SIAM Journal on Computing, 33(3):544-562, 2004.
4. M. Badoiu, S. Har-Peled, and P. Indyk. Approximate clustering via core-sets. In ACM Symposium on Theory of Computing, pages 250-257, 2002.
5. O. Benjelloun, A. D. Sarma, A. Y. Halevy, and J. Widom. Uldbs: Databases with uncertainty and lineage. In International Conference on Very Large Data Bases, 2006.
6. M. Charikar, S. Khuller, D. M. Mount, and G. Narasimhan. Algorithms for facility location problems with outliers. In ACM-SIAM Symposium on Discrete Algorithms, pages 642-651, 2001.
7. M. Chau, R. Cheng, B. Kao, and J. Ngai: Uncertain data mining: An example in clustering location data. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2006.
8. G. Cormode and M. N. Garofalakis. Sketching probabilistic data streams. In Proceedings of ACM SIGMOD International Conference on Management of Data, pages 281-292, 2007.
9. N. N. Dalvi and D. Suciu. Efficient query evaluation on probabilistic databases. VLDB J., 16(4):523-544, 2007.
10. A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39(1):1-38, 1977.
11. J. C. Dunn. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3:32-57, 1973.
12. M. Dyer and A. Frieze. A simple heuristic for the p-center problem. Operations Research Letters, 3:285-288, 1985.
13. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, page 226, 1996.
14. D. Feldman, M. Monemizadeh, and C. Sohler. A PTAS for k-means clustering based on weak coresets. In Symposium on Computational Geometry, 2007.
15. T. F. Gonzalez. Clustering to minimize the maximum intercluster distance. Theoretical Computer Science, 38(2-3):293-306, 1985.
16. S. Guha, R. Rastogi, and K. Shim. CURE: An efficient clustering algorithm for large databases. In Proceedings of ACM SIGMOD International Conference on Management of Data, pages 73-84, 1998.
17. S. Har-Peled. Geometric approximation algorithms. http://valis.cs.uiuc. edu/~sariel/teach/notes/aprx/book.pdf, 2007.
18. S. Har-Peled and S. Mazumdar. On coresets for k-means and k-median clustering. In ACM Symposium on Theory of Computing, pages 291-300, 2004.
19. D. Hochbaum and D. Shmoys. A best possible heuristic for the k-center problem. Mathematics of Operations Research, 10(2): 180-184, May 1985.
20. M. Inaba, N. Katoh, and H. Imai. Applications of weighted voronoi diagrams and randomization to variance-based k-clustering (extended abstract). In Symposium on Computational Geometry, pages 332-339, 1994.
21. P. Indyk. Algorithms for dynamic geometric problems over data streams. In ACM Symposium on Theory of Computing, 2004.
22. T. S. Jayram, A. McGregor, S. Muthukrishnan, and E. Vee. Estimating statistical aggregates on probabilistic data streams. In ACM Symposium on Principles of Database Systems, pages 243-252, 2007.
23. S. G. Kolliopoulos and S. Rao. A nearly linear-time approximation scheme for the euclidean k-median problem. In Proceedings of European Symposium on Algorithms, 1999.
24. A. Kumar, Y. Sabharwal, and S. Sen. A simple linear time (1+ε)-approximation algorithm for k-means clustering in any dimensions. In IEEE Symposium on Foundations of Computer Science, 2004.
25. J. B. MacQueen. Some method for the classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Structures, pages 281-297, 1967.
26. W. K. Ngai, B. Kao, C. K. Chui, R. Cheng, M. Chau, and K. Y. Yip. Efficient clustering of uncertain data. In IEEE International Conference on Data Mining, 2006.
27. R. Panigrahy and S. Vishwanathan. An O(log* n) approximation algorithm for the asymmetric p-center problem. J. Algorithms, 27(2):259-268, 1998.
28. T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: an efficient data clustering method for very large databases. In Proceedings of ACM SIGMOD International Conference on Management of Data, pages 103-114, 1996.