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Volumn , Issue , 2005, Pages 877-885

How fast is the k-means method?

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; HEURISTIC METHODS; ITERATIVE METHODS; PROBABILITY; SET THEORY;

EID: 20744439992     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (29)

References (16)
  • 1
    • 0032156828 scopus 로고    scopus 로고
    • Polynomial time approximation schemes for euclidean tsp and other geometric problems
    • Sep
    • S. Arora. Polynomial time approximation schemes for euclidean tsp and other geometric problems. J. Assoc. Comput. Mach., 45(5):753-782, Sep 1998.
    • (1998) J. Assoc. Comput. Mach. , vol.45 , Issue.5 , pp. 753-782
    • Arora, S.1
  • 2
    • 9444237880 scopus 로고    scopus 로고
    • How fast is k-means?
    • number 2777 in Lect. Notes in Comp. Sci.
    • S. Dasgupta. How fast is k-means? In Proc. 16th Annu. Comp. Learn. Theo., number 2777 in Lect. Notes in Comp. Sci., page 735, 2003.
    • (2003) Proc. 16th Annu. Comp. Learn. Theo. , pp. 735
    • Dasgupta, S.1
  • 4
    • 0033317391 scopus 로고    scopus 로고
    • Centroidal voronoi tessellations; Applications and algorithms
    • Q. Du, V. Faber, and M. Gunzburger. Centroidal voronoi tessellations; Applications and algorithms. SIAM Review, 41(4):637-676, 1999.
    • (1999) SIAM Review , vol.41 , Issue.4 , pp. 637-676
    • Du, Q.1    Faber, V.2    Gunzburger, M.3
  • 8
    • 0027928863 scopus 로고
    • Applications of weighted voronoi diagrams and randomization to variance-based k-clustering
    • M. Inaba, N. Katoh, and H. Imai. Applications of weighted voronoi diagrams and randomization to variance-based k-clustering. In Proc. 10th Annu. ACM Sympos. Comput. Geom., pages 332-339, 1994.
    • (1994) Proc. 10th Annu. ACM Sympos. Comput. Geom. , pp. 332-339
    • Inaba, M.1    Katoh, N.2    Imai, H.3
  • 10
    • 11244288693 scopus 로고    scopus 로고
    • A simple linear time (1+ε)-approximation algorithm for k-means clustering in any dimensions
    • page to appear
    • A. Kumar, Y. Sabharwal, and S. Sen. A simple linear time (1+ε)-approximation algorithm for k-means clustering in any dimensions. In Proc. 45th Annu. IEEE Sympos. Found. Comput. Sci., page to appear, 2004.
    • (2004) Proc. 45th Annu. IEEE Sympos. Found. Comput. Sci.
    • Kumar, A.1    Sabharwal, Y.2    Sen, S.3
  • 12
    • 0001457509 scopus 로고
    • Some methods for classifications and analysis of multivariate observations
    • Unversity of California Press, Berkeley
    • J. MacQueen. Some methods for classifications and analysis of multivariate observations. In Proc. fifth Berkeley symp. math. stat. and prob., pages 281-297. Unversity of California Press, Berkeley, 1967.
    • (1967) Proc. Fifth Berkeley Symp. Math. Stat. and Prob. , pp. 281-297
    • MacQueen, J.1
  • 13
    • 0034417244 scopus 로고    scopus 로고
    • On approximate geometric k-clustering
    • J. Matoušek. On approximate geometric k-clustering Discrete Comput. Geom., 24:61-84, 2000.
    • (2000) Discrete Comput. Geom. , vol.24 , pp. 61-84
    • Matoušek, J.1
  • 15
    • 0000963889 scopus 로고
    • Strong consistency of k-means clustering
    • D. Pollard. Strong consistency of k-means clustering. Annals of Statistics, 9:135-140, 1981.
    • (1981) Annals of Statistics , vol.9 , pp. 135-140
    • Pollard, D.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.