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Statistical theory in clustering


  • J. Hartigan


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Suggested Citation

  • J. Hartigan, 1985. "Statistical theory in clustering," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 63-76, December.
  • Handle: RePEc:spr:jclass:v:2:y:1985:i:1:p:63-76
    DOI: 10.1007/BF01908064

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    References listed on IDEAS

    1. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
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    Cited by:

    1. Véronique Cariou & Stéphane Verdun & Emmanuelle Diaz & El Qannari & Evelyne Vigneau, 2009. "Comparison of three hypothesis testing approaches for the selection of the appropriate number of clusters of variables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 3(3), pages 227-241, December.
    2. Antonio Punzo & Paul. D. McNicholas, 2017. "Robust Clustering in Regression Analysis via the Contaminated Gaussian Cluster-Weighted Model," Journal of Classification, Springer;The Classification Society, vol. 34(2), pages 249-293, July.
    3. Z. Volkovich & Z. Barzily & G.-W. Weber & D. Toledano-Kitai & R. Avros, 2012. "An application of the minimal spanning tree approach to the cluster stability problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(1), pages 119-139, March.
    4. Mitzi Montoya-Weiss & Roger J. Calantone, 1999. "Development and Implementation of a Segment Selection Procedure for Industrial Product Markets," Marketing Science, INFORMS, vol. 18(3), pages 373-395.
    5. Asmild, Mette & Hougaard, Jens Leth & Olesen, Ole B., 2013. "Testing over-representation of observations in subsets of a DEA technology," European Journal of Operational Research, Elsevier, vol. 230(1), pages 88-96.
    6. Hunt, Lynette A. & Basford, Kaye E., 2016. "Comparing classical criteria for selecting intra-class correlated features in Multimix," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 350-366.
    7. Sumit Agarwal & Brent W. Ambrose, 2008. "Does it pay to read your junk mail? evidence of the effect of advertising on home equity credit choices," Working Paper Series WP-08-09, Federal Reserve Bank of Chicago, revised 2008.
    8. Maciejowska, Katarzyna, 2013. "Assessing the number of components in a normal mixture: an alternative approach," MPRA Paper 50303, University Library of Munich, Germany.
    9. Joanna Tyrowicz, 2007. "The OCA Theory and Its Empirical Application for the EMU," Gospodarka Narodowa, Warsaw School of Economics, issue 5-6, pages 45-60.
    10. Ranjan Maitra & Ivan P. Ramler, 2009. "Clustering in the Presence of Scatter," Biometrics, The International Biometric Society, vol. 65(2), pages 341-352, June.
    11. Lee, David & Li, Wai Keung & Wong, Tony Siu Tung, 2012. "Modeling insurance claims via a mixture exponential model combined with peaks-over-threshold approach," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 538-550.
    12. Carlson, Andrea & Kinsey, Jean D. & Nadav, Carmel, 1998. "Who Eats What, When, And From Where?," Working Papers 14312, University of Minnesota, The Food Industry Center.
    13. Hien Nguyen & Geoffrey McLachlan, 2015. "Maximum likelihood estimation of Gaussian mixture models without matrix operations," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 371-394, December.
    14. Z. Volkovich & D. Toledano-Kitai & G.-W. Weber, 2013. "Self-learning K-means clustering: a global optimization approach," Journal of Global Optimization, Springer, vol. 56(2), pages 219-232, June.
    15. McLachlan, G. J. & Khan, N., 2004. "On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 90-105, July.


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