Analysis of k-means++ for separable data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7408 LNCS, Issue , 2012, Pages 591-602

  Jaiswal, Ragesh ^a   Garg, Nitin ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION RATIOS; BREGMAN DIVERGENCES; DISTANCE MEASURE; K-CENTER; K-MEANS; LLOYD'S METHOD; OPTIMAL COSTS; POINT SET; PROBLEM INSTANCES; SAMPLING ALGORITHM; SAMPLING-BASED ALGORITHMS; SEPARATION CONDITION; SQUARED EUCLIDEAN DISTANCE;

COMBINATORIAL OPTIMIZATION; GEOMETRY; LEARNING ALGORITHMS; PROBABILITY; SEPARATION;

APPROXIMATION ALGORITHMS;

EID: 84865280736     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-32512-0_50     Document Type: Conference Paper

References (10)

1. Ackermann, M.R.: Algorithms for the Bregman k-Median Problem. PhD thesis, University of Paderborn, Department of Computer Science (2009)
2. Ackermann, M.R., Blömer, J.: Bregman Clustering for Separable Instances. In: Kaplan, H. (ed.) SWAT 2010. LNCS, vol. 6139, pp. 212-223. Springer, Heidelberg (2010)
3. Aggarwal, A., Deshpande, A., Kannan, R.: Adaptive Sampling for k-Means Clustering. In: Dinur, I., Jansen, K., Naor, J., Rolim, J. (eds.) APPROX and RANDOM 2009. LNCS, vol. 5687, pp. 15-28. Springer, Heidelberg (2009)
4. Ailon, N., Jaiswal, R., Monteleoni, C.: Streaming k-means approximation. In: Advances in Neural Information Processing Systems, vol. 22, pp. 10-18 (2009)
5. Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2007), pp. 1027-1035 (2007)
6. Balcan, M.-F., Blum, A., Gupta, A.: Approximate clustering without the approximation. In: Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2009), pp. 1068-1077 (2009)
7. Banerjee, A., Merugu, S., Dhillon, I.S., Ghosh, J.: Clustering with Bregman divergences. Journal of Machine Learning Research 6, 1705-1749 (2005)
8. Brunsch, T., Röglin, H.: A Bad Instance for k-Means++. In: Ogihara, M., Tarui, J. (eds.) TAMC 2011. LNCS, vol. 6648, pp. 344-352. Springer, Heidelberg (2011)
9. Mahajan, M., Nimbhorkar, P., Varadarajan, K.: The Planar k-Means Problem is NP-Hard. In: Das, S., Uehara, R. (eds.) WALCOM 2009. LNCS, vol. 5431, pp. 274-285. Springer, Heidelberg (2009)
10. Ostrovsky, R., Rabani, Y., Schulman, L.J., Swamy, C.: The effectiveness of lloyd-type methods for the k-means problem. In: Proc. 47th IEEE FOCS, pp. 165-176 (2006)