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Volumn 37, Issue 2, 2008, Pages 135-143

Determining the number of clusters in cluster analysis

Author keywords

62H30; 91C20; Bayesian information criterion; Cluster analysis; EM algorithm; Gibbs sampler; Laplace Metropolis criteria; Maximum a posteriori; Mixture model; Modified Fisher's criteria; primary; secondary

Indexed keywords


EID: 43049127228     PISSN: 12263192     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jkss.2007.10.004     Document Type: Article
Times cited : (12)

References (19)
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  • 3
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    • Celeux, G.1    Govaert, G.2
  • 7
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    • The use of multiple measurements in taxonomic problems
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    • (1936) Annals of Eugenics , vol.7 , Issue.2 , pp. 179-188
    • Fisher, R.A.1
  • 8
    • 22844453564 scopus 로고    scopus 로고
    • MCLUST: Software for model-based clustering
    • Fraley C., and Raftery A.E. MCLUST: Software for model-based clustering. Journal of Classification 16 (1999) 297-306
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    • Fraley, C.1    Raftery, A.E.2
  • 10
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    • Fraley, C.1    Raftery, A.E.2
  • 12
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    • Lewis, S. M., & Raftery, A. E. (1994). Estimating Bayes factors via posterior simulation with the Laplace-Metropolis estimator, Technical report, 279. Department of Statistics, University of Washington
    • Lewis, S. M., & Raftery, A. E. (1994). Estimating Bayes factors via posterior simulation with the Laplace-Metropolis estimator, Technical report, 279. Department of Statistics, University of Washington
  • 18
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    • Estimating the dimension of a model
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  • 19
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    • Bayesian computation via the Gibbs sampler and related Markov Chain Monte Carlo Methods (with discussion)
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    • Smith1    Roberts2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.