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Volumn 2, Issue , 2008, Pages 431-438

Knee point detection on bayesian information criterion

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; CLUSTER ANALYSIS;

EID: 57649143299     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2008.154     Document Type: Conference Paper
Times cited : (46)

References (14)
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    • X.L. Hu and L. Xu. Investigation on several model selection criteria for determining the number of cluster. Neural Information Processing, Vol. 4, No. 1, July 2004.
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    • How many clusters? Which clustering method? Answers via model-based cluster analysis
    • C. Frayley and A. Raftery. How many clusters? Which clustering method? Answers via model-based cluster analysis. The computer Journal, Vol. 41, No. 8, pp. 578-588, 1998.
    • (1998) The computer Journal , vol.41 , Issue.8 , pp. 578-588
    • Frayley, C.1    Raftery, A.2
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    • Detecting features in spatial point process with clutter via model-based clustering
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    • Dasgupta, A.1    Raftery, A.2
  • 9
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    • Who belongs in the family?
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    • (1953) Psychometrika , vol.18 , pp. 267-276
    • Thorndike, R.L.1
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    • A criterion for determining the number of groups in a data set using sum-of-squares clustering
    • Mar
    • W.J. Krzanowski, Y.T. Lai, A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics, Vol. 44, No. 1 (Mar., 1988), pp.23-34.
    • (1988) Biometrics , vol.44 , Issue.1 , pp. 23-34
    • Krzanowski, W.J.1    Lai, Y.T.2
  • 12
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    • Model-Based Gaussian and Non-Gaussian Clustering
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    • A reference Bayesian test for nested Hypotheses and its relationship to the Schwarz Criterion
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    • Randomized local search algorithm for the clustering problem
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.