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Volumn , Issue , 2006, Pages 775-784

On the lower bound of local optimums in k-means algorithm

Author keywords

[No Author keywords available]

Indexed keywords

GLOBAL OPTIMAL SOLUTION; K-MEANS ALGORITHM; TIME CONSUMPTION;

EID: 56449089516     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2006.118     Document Type: Conference Paper
Times cited : (7)

References (16)
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    • D. Arthur and S. Vassilvitskii. How slow is the k-means method? In SoCG, pages 144-153, 2006.
    • (2006) SoCG , pp. 144-153
    • Arthur, D.1    Vassilvitskii, S.2
  • 2
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    • Convergence properties of the K-means algorithms
    • L. Bottou and Y. Bengio. Convergence properties of the K-means algorithms. In NIPS, pages 585-592, 1995.
    • (1995) In NIPS , pp. 585-592
    • Bottou, L.1    Bengio, Y.2
  • 3
    • 0002550769 scopus 로고    scopus 로고
    • Refining initial points for K-Means clustering
    • P S. Bradley and U. M. Fayyad. Refining initial points for K-Means clustering. In ICML, pages 91-99, 1998.
    • (1998) ICML , pp. 91-99
    • Bradley, P.S.1    Fayyad, U.M.2
  • 5
    • 14344257496 scopus 로고    scopus 로고
    • K-means clustering via principal component analysis
    • C. H. Q. Ding and X. He. K-means clustering via principal component analysis. In ICML, 2004.
    • (2004) ICML
    • Ding, C.H.Q.1    He, X.2
  • 6
    • 1942485278 scopus 로고    scopus 로고
    • Using the triangle inequality to accelerate k-means
    • C. Elkan. Using the triangle inequality to accelerate k-means. In ICML, pages 147-153, 2003.
    • (2003) ICML , pp. 147-153
    • Elkan, C.1
  • 8
    • 20744439992 scopus 로고    scopus 로고
    • How fast is the k-means method?
    • S. Har-Peled and B. Sadri. How fast is the k-means method? In SODA, pages 877-885, 2005.
    • (2005) SODA , pp. 877-885
    • Har-Peled, S.1    Sadri, B.2
  • 9
    • 0027928863 scopus 로고
    • Applications of weighted voronoi diagrams and randomization to variance-based - clustering (extended abstract)
    • M. Inaba, N. Katoh, and H. Imai. Applications of weighted voronoi diagrams and randomization to variance-based - clustering (extended abstract). In Symposium on Computational Geometry, pages 332-339, 1994.
    • (1994) Symposium on Computational Geometry , pp. 332-339
    • Inaba, M.1    Katoh, N.2    Imai, H.3
  • 12
    • 11244288693 scopus 로고    scopus 로고
    • A simple linear time (1+ε)-approximation algorithm for k-means clustering in any dimensions
    • A. Kumar, Y. Sabharwal, and S. Sen. A simple linear time (1+ε)-approximation algorithm for k-means clustering in any dimensions. In FOCS, pages 454-462, 2004.
    • (2004) FOCS , pp. 454-462
    • Kumar, A.1    Sabharwal, Y.2    Sen, S.3
  • 15
    • 0002738562 scopus 로고    scopus 로고
    • Accelerating exact k -means algorithms with geometric reasoning
    • D. Pelleg and A. Moore. Accelerating exact k -means algorithms with geometric reasoning. In Knowledge Discovery and Data Mining, pages 277-281, 1999.
    • (1999) Knowledge Discovery and Data Mining , pp. 277-281
    • Pelleg, D.1    Moore, A.2
  • 16
    • 0030157145 scopus 로고    scopus 로고
    • BIRCH: An efficient data clustering method for very large databases
    • T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: an efficient data clustering method for very large databases. In SIGMOD Conference, pages 103-114, 1996.
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    • Zhang, T.1    Ramakrishnan, R.2    Livny, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.