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Volumn , Issue , 2009, Pages 461-470

Improved smoothed analysis of the k-means method

Author keywords

[No Author keywords available]

Indexed keywords

POLYNOMIAL APPROXIMATION;

EID: 70349088984     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611973068.51     Document Type: Conference Paper
Times cited : (22)

References (13)
  • 3
    • 38749098136 scopus 로고    scopus 로고
    • Worst-case and smoothed analysis of the ICP algorithm, with an application to the k-means method
    • IEEE Computer Society
    • David Arthur and Sergei Vassilvitskii. Worst-case and smoothed analysis of the ICP algorithm, with an application to the k-means method. In Proc. of the 47th Ann. IEEE Symp. on Foundations of Comp. Science (FOCS), pages 153-164. IEEE Computer Society, 2006.
    • (2006) Proc. of the 47th Ann. IEEE Symp. on Foundations of Comp. Science (FOCS) , pp. 153-164
    • Arthur, D.1    Vassilvitskii, S.2
  • 7
    • 11244293701 scopus 로고    scopus 로고
    • How fast is the k-means method?
    • Sariel Har-Peled and Bardia Sadri. How fast is the k-means method? Algorithmica, 41(3):185-202, 2005.
    • (2005) Algorithmica , vol.41 , Issue.3 , pp. 185-202
    • Har-Peled, S.1    Sadri, B.2
  • 8
    • 0033707141 scopus 로고    scopus 로고
    • Variance-based k-clustering algorithms by Voronoi diagrams and randomization
    • Mary Inaba, Naoki Katoh, and Hiroshi Imai. Variance-based k-clustering algorithms by Voronoi diagrams and randomization. IEICE Transactions on Information and Systems, E83-D(6):1199-1206, 2000. (Pubitemid 30884852)
    • (2000) IEICE Transactions on Information and Systems , vol.E83-D , Issue.6 , pp. 1199-1206
    • Inaba, M.1    Katoh, N.2    Imai, H.3
  • 9
    • 11244288693 scopus 로고    scopus 로고
    • A simple linear time (1 + ε)-approximation algorithm for k-means clustering in any dimensions
    • Proceedings - 45th Annual IEEE Symposium on Foundations of Computer Sciences, FOCS 2004
    • Amit Kumar, Yogish Sabharwal, and Sandeep Sen. A simple linear time (1 + ε)-approximation algorithm for k-means clustering in any dimensions. In Proc. of the 45th Ann. IEEE Symp. on Foundations of Comp. Science (FOCS), pages 454-462, 2004. (Pubitemid 40575306)
    • (2004) Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS , pp. 454-462
    • Kumar, A.1    Sabharwal, Y.2    Sen, S.3
  • 11
    • 0034417244 scopus 로고    scopus 로고
    • On approximate geometric k-clustering
    • Jiří Matoušek. On approximate geometric k-clustering. Discrete and Computational Geometry, 24(1):61-84, 2000.
    • (2000) Discrete and Computational Geometry , vol.24 , Issue.1 , pp. 61-84
    • Matoušek, J.1
  • 12
    • 4243066295 scopus 로고    scopus 로고
    • Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time
    • Daniel A. Spielman and Shang-Hua Teng. Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time. Journal of the ACM, 51(3):385-463, 2004.
    • (2004) Journal of the ACM , vol.51 , Issue.3 , pp. 385-463
    • Spielman, D.A.1    Teng, S.-H.2
  • 13
    • 38049132149 scopus 로고    scopus 로고
    • Smoothed analysis of algorithms and heuristics: Progress and open questions
    • Luis M. Pardo, Allan Pinkus, Endre Süli, and Michael J. Todd, editors, Cambridge University Press
    • Daniel A. Spielman and Shang-Hua Teng. Smoothed analysis of algorithms and heuristics: Progress and open questions. In Luis M. Pardo, Allan Pinkus, Endre Süli, and Michael J. Todd, editors, Foundations of Computational Mathematics, Santander 2005, pages 274-342. Cambridge University Press, 2006.
    • (2006) Foundations of Computational Mathematics, Santander 2005 , pp. 274-342
    • Spielman, D.A.1    Teng, S.-H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.