Streaming k-means approximation

Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Volumn , Issue , 2009, Pages 10-18

  Ailon, Nir ^a   Jaiswal, Ragesh ^b   Monteleoni, Claire ^b  

^a GOOGLE INC   (United States)

^b Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS;

BATCH ALGORITHMS; DATA RESOURCES; K-CENTER; K-MEANS; LIGHT WEIGHT; MASSIVE DATA SETS; ONE-PASS; PSEUDO-APPROXIMATIONS; RESOURCECONSTRAINED DEVICES; SIMPLE++;

CLUSTERING ALGORITHMS;

EID: 84858709907     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Ankit Aggarwal, Amit Deshpande and Ravi Kannan: Adaptive Sampling for k-means Clustering. APPROX, 2009.
2. Nir Ailon and Edo Liberty: Correlation Clustering Revisited: The "True" Cost of Error Minimization Problems. To appear in ICALP 2009.
3. Noga Alon, Yossi Matias, and Mario Szegedy.: The space complexity of approximating the frequency moments. In Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, pages 20-29, 1996.
4. David Arthur and Sergei Vassilvitskii: Worst-case and smoothed analyses of the icp algorithm, with an application to the k-means method. FOCS, 2006
5. David Arthur and Sergei Vassilvitskii: k-means++: the advantages of careful seeding. SODA, 2007.
6. 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 of Computing, 33(3):544-562, 2004.
7. A. Asuncion and D.J. Newman: UCI Machine Learning Repository. http://www.ics.uci.edu/~mlearn/MLRepository.html, University of California, Irvine, School of Information and Computer Sciences, 2007.
8. Maria-Florina Balcan, Avrim Blum, and Anupam Gupta: Approximate Clustering without the Approximation. SODA, 2009.
9. S. Ben-David and U. von Luxburg: Relating clustering stability to properties of cluster boundaries. COLT, 2008
10. Moses Charikar and Liadan O'Callaghan and Rina Panigrahy: Better streaming algorithms for clustering problems. STOC, 2003.
11. Moses Charikar and Sudipto Guha: Improved combinatorial algorithms for the facility location and k-medians problem. FOCS, 1999.
12. M. Charikar, S. Guha, E Tardos, and D. Shmoys: A Constant Factor Approximation Algorithm for the k-Median Problem. Journal of Computer and System Sciences, 2002.
13. Kamalika Chaudhuri and Satish Rao: Learning Mixtures of Product Distributions using Correlations and Independence. COLT, 2008.
14. Sanjoy Dasgupta.: Course notes, CSE 291: Topics in unsupervised learning. http://www-cse.ucsd.edu/dasgupta/291/index.html, University of California, San Diego, Spring 2008.
15. T. F. Gonzalez: Clustering to minimize the maximum intercluster distance. Theoretical Computer Science, 38, pages 293-306, 1985.
16. Sudipto Guha, Adam Meyerson, Nina Mishra, Rajeev Motwani, and Liadan O'Callaghan: Clustering Data Streams: Theory and Practice. IEEE Transactions on Knowledge and Data Engineering, 15(3): 515-528, 2003.
17. Piotr Indyk: Sublinear Time Algorithms for Metric Space Problems. STOC, 1999.
18. K. Jain and Vijay Vazirani: Approximation Algorithms for Metric Facility Location and k-Median Problems Using the Primal-Dual Schema and Lagrangian Relaxation. Journal of the ACM. 2001.
19. T. Kanungo, D. M. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Y. Wu: A Local Search Approximation Algorithm for k-Means Clustering, Computational Geometry: Theory and Applications, 28, 89-112, 2004.
20. J. Lin and J. S. Vitter: Approximation Algorithms for Geometric Median Problems. Information Processing Letters, 1992.
21. Andrew McGregor: Processing Data Streams. Ph.D. Thesis, Computer and Information Science, University of Pennsylvania, 2007.
22. S. Muthukrishnan: Data Streams: Algorithms and Applications, NOW Publishers, Inc., Hanover MA
23. Rafail Ostrovsky, Yuval Rabani, Leonard J. Schulman, Chaitanya Swamy: The effectiveness of Lloyd-type methods for the k-means problem. FOCS, 2006.
24. V. Vempala and G. Wang: A spectral algorithm of learning mixtures of distributions. pages 113-123, FOCS, 2002.