Scalable k-means by ranked retrieval

WSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining

Volumn , Issue , 2014, Pages 233-242

  Broder, Andrei ^a   Garcia Pueyo, Lluis ^a   Josifovski, Vanja ^a   Vassilvitskii, Sergei ^a   Venkatesan, Srihari ^b  

^a GOOGLE INC   (United States)

^b xAd   (United States)

Author keywords

k means; wand

Indexed keywords

CLUSTERING ALGORITHMS; COSTS; DATA MINING; INFORMATION RETRIEVAL; WEBSITES;

CLUSTER MEMBERSHIPS; COMPUTATIONAL BOTTLENECKS; HIGH DIMENSIONAL SPACES; IMPROVE PERFORMANCE; INTERMEDIATE RESULTS; K-MEANS; K-MEANS CLUSTERING ALGORITHM; WAND;

ITERATIVE METHODS;

EID: 84906872122     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2556195.2556260     Document Type: Conference Paper

References (35)

1. N. Ailon, R. Jaiswal, and C. Monteleoni. Streaming k-means approximation. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, NIPS 22. 2009.
2. A. Andoni and P. Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun. ACM, 51(1):117-122, Jan. 2008.
3. D. Arthur, B. Manthey, and H. Röglin. Smoothed analysis of the k-means method. J. ACM, 58(5):19, 2011.
4. D. Arthur and S. Vassilvitskii. How slow is the k-means method? In Symposium on Computational Geometry, 2006.
5. D. Arthur and S. Vassilvitskii. K-means++: the advantages of careful seeding. In N. Bansal, K. Pruhs, and C. Stein, editors, SODA. SIAM, 2007.
6. R. A. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1999.
7. B. Bahmani, B. Moseley, A. Vattani, R. Kumar, and S. Vassilvitskii. Scalable k-means++. PVLDB, 5(7):622-633, 2012.
8. R. J. Bayardo, Y. Ma, and R. Srikant. Scaling up all pairs similarity search. In Proceedings of the 16th international conference on World Wide Web, WWW '07, 2007.
9. R. E. Bellman. Dynamic Programming. Princeton University Press, Princeton, NJ, USA, 1957.
10. V. Braverman, A. Meyerson, R. Ostrovsky, A. Roytman, M. Shindler, and B. Tagiku. Streaming k-means on well-clusterable data. In Proceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms, SODA '11, pages 26-40. SIAM, 2011.
11. A. Broder, M. Fontoura, E. Gabrilovich, A. Joshi, V. Josifovski, and T. Zhang. Robust classification of rare queries using web knowledge. In SIGIR'07, 2007.
12. A. Z. Broder, D. Carmel, M. Herscovici, A. Soffer, and J. Y. Zien. Efficient query evaluation using a two-level retrieval process. In CIKM, 2003.
13. M. Charikar, L. O'Callaghan, and R. Panigrahy. Better streaming algorithms for clustering problems. In L. L. Larmore and M. X. Goemans, editors, STOC. ACM, 2003.
14. C. Elkan. Using the triangle inequality to accelerate k-means. In ICML, pages 147-153, 2003.
15. M. Fontoura, V. Josifovski, J. Liu, S. Venkatesan, X. Zhu, and J. Y. Zien. Evaluation strategies for top-k queries over memory-resident inverted indexes. PVLDB, 4(11), 2011.
16. A. S. Foundation, I. Drost, T. Dunning, J. Eastman, O. Gospodnetic, G. Ingersoll, J. Mannix, S. Owen, and K. Wettin. Apache mahout, 2010. http://mloss.org/software/view/144/.
17. S. Guha, N. Mishra, R. Motwani, and L. O'Callaghan. Clustering data streams. In FOCS, 2000.
18. V. Hautamäki, S. Cherednichenko, I. Kärkkäinen, T. Kinnunen, and P. Fränti. Improving k-means by outlier removal. In SCIA, 2005.
19. R. Jin, A. Goswami, and G. Agrawal. Fast and exact out-of-core and distributed k-means clustering. Knowl. Inf. Syst., 10(1):17-40, July 2006. (Pubitemid 44056906)
20. T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell., 24(7), 2002.
21. T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu. A local search approximation algorithm for k-means clustering. Comput. Geom., 28(2-3), 2004.
22. P. Lacour, C. Macdonald, and I. Ounis. Efficiency comparison of document matching techniques. In Efficiency Issues in Information Retrieval Workshop; European Conference for Information Retrieval, 2008.
23. N. Lester, A. Moffat, W. Webber, and J. Zobel. Space-limited ranked query evaluation using adaptive pruning. In WISE, 2005.
24. S. P. Lloyd. Least squares quantization in pcm. IEEE Transactions on Information Theory, 28, 1982.
25. A. Meyerson. Online facility location. In FOCS, 2001.
26. A. Moffat and J. Zobel. Self-indexing inverted files for fast text retrieval. ACM Transactions on Information Systems, 14(4), 1996.
27. L. Parsons, E. Haque, and H. Liu. Subspace clustering for high dimensional data: a review. SIGKDD Explor., 6(1), 2004.
28. D. Pelleg and A. Moore. Accelerating exact k-means algorithms with geometric reasoning. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '99, pages 277-281, New York, NY, USA, 1999. ACM.
29. D. Pelleg and A. Moore. X-means: Extending k-means with efficient estimation of the number of clusters. In In Proceedings of the 17th International Conf. on Machine Learning, pages 727-734. Morgan Kaufmann, 2000.
30. D. Sculley. Web-scale k-means clustering. In WWW, 2010.
31. M. Shindler, A. Wong, and A. W. Meyerson. Fast and accurate k-means for large datasets. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, editors, Advances in Neural Information Processing Systems 24, pages 2375-2383. 2011.
32. A. Strehl, J. Ghosh, and R. J. Mooney. Impact of similarity measures on web-page clustering. In Proc. AAAI Workshop on AI for Web Search (AAAI 2000), Austin, pages 58-64. AAAI/MIT Press, July 2000.
33. H. R. Turtle and J. Flood. Query evaluation: Strategies and optimizations. Information Processing and Management, 31(6), 1995.
34. A. Vattani. k-means requires exponentially many iterations even in the plane. Discrete & Computational Geometry, 45(4):596-616, 2011.
35. J. Zobel and A. Moffat. Inverted files for text search engines. ACM Computing Surveys, 38(2), 2006.