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Large-sample results for optimization-based clustering methods

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  • Peter Bryant

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  • Peter Bryant, 1991. "Large-sample results for optimization-based clustering methods," Journal of Classification, Springer;The Classification Society, vol. 8(1), pages 31-44, January.
    • Handle: RePEc:spr:jclass:v:8:y:1991:i:1:p:31-44
    DOI: 10.1007/BF02616246
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    References listed on IDEAS

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    1. William Day & Herbert Edelsbrunner, 1985. "Investigation of proportional link linkage clustering methods," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 239-254, December.
    2. Michael Windham, 1987. "Parameter modification for clustering criteria," Journal of Classification, Springer;The Classification Society, vol. 4(2), pages 191-214, September.
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    Cited by:

    1. Zaheer Ahmed & Alberto Cassese & Gerard Breukelen & Jan Schepers, 2023. "E-ReMI: Extended Maximal Interaction Two-mode Clustering," Journal of Classification, Springer;The Classification Society, vol. 40(2), pages 298-331, July.
    2. Johann Bacher, 2000. "A Probabilistic Clustering Model for Variables of Mixed Type," Quality & Quantity: International Journal of Methodology, Springer, vol. 34(3), pages 223-235, August.
    3. Francesco Dotto & Alessio Farcomeni & Luis Angel García-Escudero & Agustín Mayo-Iscar, 2017. "A fuzzy approach to robust regression clustering," 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. 11(4), pages 691-710, December.
    4. Wayne DeSarbo & Duncan Fong & John Liechty & M. Kim Saxton, 2004. "A hierarchical bayesian procedure for two-mode cluster analysis," Psychometrika, Springer;The Psychometric Society, vol. 69(4), pages 547-572, December.
    5. Teague R. Henry & Kathleen M. Gates & Mitchell J. Prinstein & Douglas Steinley, 2020. "Modeling Heterogeneous Peer Assortment Effects Using Finite Mixture Exponential Random Graph Models," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 8-34, March.
    6. Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 195-212, September.
    7. Alessio Farcomeni & Antonio Punzo, 2020. "Robust model-based clustering with mild and gross outliers," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 989-1007, December.
    8. Gallegos, María Teresa & Ritter, Gunter, 2013. "Strong consistency of k-parameters clustering," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 14-31.
    9. Fritz, Heinrich & García-Escudero, Luis A. & Mayo-Iscar, Agustín, 2013. "A fast algorithm for robust constrained clustering," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 124-136.
    10. Langevin, R.;, 2024. "Consistent Estimation of Finite Mixtures: An Application to Latent Group Panel Structures," Health, Econometrics and Data Group (HEDG) Working Papers 24/16, HEDG, c/o Department of Economics, University of York.
    11. Kerekes, Monika, 2009. "Growth miracles and failures in a Markov switching classification model of growth," Discussion Papers 2009/11, Free University Berlin, School of Business & Economics.

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