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Volumn 56, Issue 3, 2012, Pages 468-477

Selection of the number of clusters via the bootstrap method

Author keywords

Cluster analysis; K means; Spectral clustering; Stability

Indexed keywords

BOOTSTRAP METHOD; CLUSTERING STABILITY; CROSS VALIDATION; ESTIMATION SCHEMES; K-MEANS; NUMBER OF CLUSTERS; REAL EXAMPLE; SELECTION CRITERIA; SPECTRAL CLUSTERING;

EID: 80455177001     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2011.09.003     Document Type: Article
Times cited : (135)

References (32)
  • 4
    • 56649114849 scopus 로고    scopus 로고
    • More on the stability of hierarchical clustering
    • Madison, WI
    • Bryant, P.; 2002. More on the stability of hierarchical clustering, Paper presented at the Classification Society of North America Meeting, Madison, WI.
    • (2002) Classification Society of North America Meeting
    • Bryant, P.1
  • 7
    • 0001468152 scopus 로고
    • More accurate confidence intervals in exponential families
    • T.J. DiCiccio, and B. Efron More accurate confidence intervals in exponential families Biometrika 79 1992 231 245
    • (1992) Biometrika , vol.79 , pp. 231-245
    • Diciccio, T.J.1    Efron, B.2
  • 8
    • 0001782309 scopus 로고
    • Jackknife-after-bootstrap standard errors and influence functions
    • B. Efron Jackknife-after-bootstrap standard errors and influence functions Journal of Royal Statistical Society, Series B 54 1992 83 127
    • (1992) Journal of Royal Statistical Society, Series B , vol.54 , pp. 83-127
    • Efron, B.1
  • 9
    • 84923818429 scopus 로고
    • Better bootstrap confidence intervals (with discussion)
    • B. Efron Better bootstrap confidence intervals (with discussion) Journal of American Statistical Association 82 1987 171 200
    • (1987) Journal of American Statistical Association , vol.82 , pp. 171-200
    • Efron, B.1
  • 11
    • 79952038641 scopus 로고    scopus 로고
    • Penalized cluster analysis with applications to family data
    • Y. Fang, and J. Wang Penalized cluster analysis with applications to family data Computational Statistics and Data Analysis 55 2011 2128 2136
    • (2011) Computational Statistics and Data Analysis , vol.55 , pp. 2128-2136
    • Fang, Y.1    Wang, J.2
  • 12
    • 0000764772 scopus 로고
    • The use of multiple measurements in taxonomic problems
    • R.A. Fisher The use of multiple measurements in taxonomic problems Annals of Eugenics 7 1936 179 188
    • (1936) Annals of Eugenics , vol.7 , pp. 179-188
    • Fisher, R.A.1
  • 16
    • 0014129195 scopus 로고
    • Hierarchical clustering schemes
    • S.C. Johnson Hierarchical clustering schemes Psychometrika 2 1967 241 254
    • (1967) Psychometrika , vol.2 , pp. 241-254
    • Johnson, S.C.1
  • 18
    • 0033196672 scopus 로고    scopus 로고
    • A cautionary note on using internal crossvalidation to select the number of clusters
    • A.W. Krieger, and P.E. Green A cautionary note on using internal crossvalidation to select the number of clusters Psychometrika 64 1999 341 353
    • (1999) Psychometrika , vol.64 , pp. 341-353
    • Krieger, A.W.1    Green, P.E.2
  • 19
    • 33646597836 scopus 로고
    • A criterion for determining the number of clusters in a data set
    • W.J. Krzanowski, and Y.T. Lai A criterion for determining the number of clusters in a data set Biometrics 44 1985 23 34
    • (1985) Biometrics , vol.44 , pp. 23-34
    • Krzanowski, W.J.1    Lai, Y.T.2
  • 20
    • 2442611856 scopus 로고    scopus 로고
    • Stability-based validation of clustering solutions
    • DOI 10.1162/089976604773717621
    • T. Lange, V. Roth, M. Braun, and J. Buhmann Stability-based validation of clustering solutions Neural Computation 16 2004 1299 1323 (Pubitemid 38640626)
    • (2004) Neural Computation , vol.16 , Issue.6 , pp. 1299-1323
    • Lange, T.1    Roth, V.2    Braun, M.L.3    Buhmann, J.M.4
  • 22
    • 0000226068 scopus 로고
    • A nearest-centroid technique for evaluating the minimum variance clustering procedure
    • R.M. McIntyre, and R.K. Blashfield A nearest-centroid technique for evaluating the minimum variance clustering procedure Multivariate Behavioral Research 15 1980 225 238
    • (1980) Multivariate Behavioral Research , vol.15 , pp. 225-238
    • McIntyre, R.M.1    Blashfield, R.K.2
  • 23
    • 34250115918 scopus 로고
    • An examination of procedures for determining the number of clusters in a dataset
    • G.W. Milligan, and M.C. Cooper An examination of procedures for determining the number of clusters in a dataset Psychometrika 50 1985 159 179
    • (1985) Psychometrika , vol.50 , pp. 159-179
    • Milligan, G.W.1    Cooper, M.C.2
  • 24
    • 84899013108 scopus 로고    scopus 로고
    • On spectral clustering: Analysis and an algorithm
    • T. Dietterich, S. Becker, Z. Ghahramani, MIT Press Cambridge
    • A. Ng, M. Jordan, and Y. Weiss On spectral clustering: analysis and an algorithm T. Dietterich, S. Becker, Z. Ghahramani, Adv. Neural. Info. Processing Sys. (NIPS2001) 2001 MIT Press Cambridge 849 856
    • (2001) Adv. Neural. Info. Processing Sys. (NIPS2001) , pp. 849-856
    • Ng, A.1    Jordan, M.2    Weiss, Y.3
  • 25
    • 85048667824 scopus 로고    scopus 로고
    • Cluster stability for finite samples
    • J. Platt, D. Koller, Y. Singer, S. Roweis, MIT Press Cambridge
    • O. Shamir, and T. Tishby Cluster stability for finite samples J. Platt, D. Koller, Y. Singer, S. Roweis, Adv. Neural Info. Processing Sys. (NIPS2007) 2007 MIT Press Cambridge 1297 1304
    • (2007) Adv. Neural Info. Processing Sys. (NIPS2007) , pp. 1297-1304
    • Shamir, O.1    Tishby, T.2
  • 28
    • 0242679438 scopus 로고    scopus 로고
    • Finding the Number of Clusters in a Dataset: An Information-Theoretic Approach
    • DOI 10.1198/016214503000000666
    • C. Sugar, and G. James Finding the number of clusters in a data set: an imformation theoretic approach Journal of American Statistical Association 98 2003 750 763 (Pubitemid 37395947)
    • (2003) Journal of the American Statistical Association , vol.98 , Issue.463 , pp. 750-763
    • Sugar, C.A.1    James, G.M.2
  • 31
    • 78651282047 scopus 로고    scopus 로고
    • Consistent selection of the number of clusters via cross-validation
    • J. Wang Consistent selection of the number of clusters via cross-validation Biometrika 97 2010 893 904
    • (2010) Biometrika , vol.97 , pp. 893-904
    • Wang, J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.