메뉴 건너뛰기




Volumn , Issue , 2009, Pages

Finding low error clusterings

Author keywords

[No Author keywords available]

Indexed keywords

C-APPROXIMATION ALGORITHMS; CLUSTERING APPLICATIONS; CLUSTERING PROBLEMS; CLUSTERINGS; CORRELATION CLUSTERING; OBJECTIVE FUNCTIONS; SIMILARITY INFORMATIONS; TARGET CLUSTERING;

EID: 84898063953     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (28)

References (29)
  • 2
    • 84898064830 scopus 로고    scopus 로고
    • Which data sets are clusterable? - A theoretical study of clusterability
    • M. Ackerman and S. Ben-David. Which data sets are clusterable? - a theoretical study of clusterability. In NIPS, 2008.
    • (2008) NIPS
    • Ackerman, M.1    Ben-David, S.2
  • 5
    • 84898068808 scopus 로고    scopus 로고
    • Learning mixtures of arbitrary Gaussians
    • S. Arora and R. Kannan. Learning mixtures of arbitrary gaussians. In STOC, 2005.
    • (2005) STOC
    • Arora, S.1    Kannan, R.2
  • 8
    • 57049163657 scopus 로고    scopus 로고
    • A discrimantive framework for clustering via similarity functions
    • M.-F. Balcan, A. Blum, and S. Vempala. A discrimantive framework for clustering via similarity functions. In STOC, 2008.
    • (2008) STOC
    • Balcan, M.-F.1    Blum, A.2    Vempala, S.3
  • 10
    • 33847677259 scopus 로고    scopus 로고
    • A framework for statistical clustering with constant time approximation for k-median and k-means clustering
    • S. Ben-David. A framework for statistical clustering with constant time approximation for k-median and k-means clustering. Machine Learning, 66(2-3), 2007.
    • (2007) Machine Learning , vol.66 , Issue.2-3
    • Ben-David, S.1
  • 12
    • 0032630441 scopus 로고    scopus 로고
    • A constant-factor approximation algorithm for the k-median problem
    • M. Charikar, S. Guha, E. Tardos, and D. B. Shmoy. A constant-factor approximation algorithm for the k-median problem. In STOC, 1999.
    • (1999) STOC
    • Charikar, M.1    Guha, S.2    Tardos, E.3    Shmoy, D.B.4
  • 20
    • 0032674516 scopus 로고    scopus 로고
    • Sublinear time algorithms for metric space problems
    • P. Indyk. Sublinear time algorithms for metric space problems. In STOC, 1999.
    • (1999) STOC
    • Indyk, P.1
  • 21
    • 0036041233 scopus 로고    scopus 로고
    • A new greedy approach for facility location problems
    • K. Jain, M. Mahdian, and A. Saberi. A new greedy approach for facility location problems. In 34th STOC, 2002.
    • (2002) 34th STOC
    • Jain, K.1    Mahdian, M.2    Saberi, A.3
  • 23
    • 33746066585 scopus 로고    scopus 로고
    • The spectral method for general mixture models
    • R. Kannan, H. Salmasian, and S. Vempala. The spectral method for general mixture models. In 18th COLT, 2005.
    • (2005) 18th COLT
    • Kannan, R.1    Salmasian, H.2    Vempala, S.3
  • 24
    • 85156277066 scopus 로고    scopus 로고
    • An impossibility theorem for clustering
    • J. Kleinberg. An impossibility theorem for clustering. In NIPS, 2002.
    • (2002) NIPS
    • Kleinberg, J.1
  • 25
    • 11244288693 scopus 로고    scopus 로고
    • A simple linear time (1 + ∈)-approximation algorithm for k-means clustering in any dimensions
    • A. Kumar, Y. Sabharwal, and S. Sen. A simple linear time (1 + ∈)-approximation algorithm for k-means clustering in any dimensions. In 45th FOCS, 2004.
    • (2004) 45th FOCS
    • Kumar, A.1    Sabharwal, Y.2    Sen, S.3
  • 26
    • 10044298988 scopus 로고    scopus 로고
    • Comparing clusterings by the variation of information
    • M. Meila. Comparing clusterings by the variation of information. In COLT, 2003.
    • (2003) COLT
    • Meila, M.1
  • 29
    • 3042606899 scopus 로고    scopus 로고
    • A spectral algorithm for learning mixture models
    • S. Vempala and G. Wang. A spectral algorithm for learning mixture models. JCSS, 68(2): 841-860, 2004.
    • (2004) JCSS , vol.68 , Issue.2 , pp. 841-860
    • Vempala, S.1    Wang, G.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.