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The mixture method of clustering applied to three-way data

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  • Kaye Basford
  • Geoffrey McLachlan

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  • Kaye Basford & Geoffrey McLachlan, 1985. "The mixture method of clustering applied to three-way data," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 109-125, December.
    • Handle: RePEc:spr:jclass:v:2:y:1985:i:1:p:109-125
    DOI: 10.1007/BF01908066
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    References listed on IDEAS

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    1. J. Carroll & Linda Clark & Wayne DeSarbo, 1984. "The representation of three-way proximity data by single and multiple tree structure models," Journal of Classification, Springer;The Classification Society, vol. 1(1), pages 25-74, December.
    2. Roger Shepard, 1962. "The analysis of proximities: Multidimensional scaling with an unknown distance function. II," Psychometrika, Springer;The Psychometric Society, vol. 27(3), pages 219-246, September.
    3. Roger Shepard, 1962. "The analysis of proximities: Multidimensional scaling with an unknown distance function. I," Psychometrika, Springer;The Psychometric Society, vol. 27(2), pages 125-140, June.
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    Citations

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    Cited by:

    1. Leonardo Salvatore Alaimo & Francesco Amato & Filomena Maggino & Alfonso Piscitelli & Emiliano Seri, 2023. "A Comparison of Migrant Integration Policies via Mixture of Matrix-Normals," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 165(2), pages 473-494, January.
    2. Vermunt, Jeroen K., 2007. "A hierarchical mixture model for clustering three-way data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5368-5376, July.
    3. Pennings, Joost M.E. & Garcia, Philip & Irwin, Scott H. & Good, Darrel L., 2003. "How To Group Market Participants? Heterogeneity In Hedging Behavior," 2003 Annual meeting, July 27-30, Montreal, Canada 21963, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Simon Blanchard & Wayne DeSarbo, 2013. "A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification," Psychometrika, Springer;The Psychometric Society, vol. 78(2), pages 322-340, April.
    5. Kumar, Sameer & Arora, Sant, 1999. "Efficient workforce scheduling for a serial processing environment: a case study at Minneapolis Star Tribune," Omega, Elsevier, vol. 27(1), pages 115-127, February.
    6. Pennings, Joost M. E. & Garcia, Philip, 2004. "Hedging behavior in small and medium-sized enterprises: The role of unobserved heterogeneity," Journal of Banking & Finance, Elsevier, vol. 28(5), pages 951-978, May.
    7. Alex Sharp & Glen Chalatov & Ryan P. Browne, 2023. "A dual subspace parsimonious mixture of matrix normal distributions," 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. 17(3), pages 801-822, September.
    8. Pieter C. Schoonees & Patrick J. F. Groenen & Michel Velden, 2022. "Least-squares bilinear clustering of three-way data," 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. 16(4), pages 1001-1037, December.
    9. Vichi, Maurizio, 1998. "Principal classifications analysis: a method for generating consensus dendrograms and its application to three-way data," Computational Statistics & Data Analysis, Elsevier, vol. 27(3), pages 311-331, May.
    10. Federico Ferraccioli & Giovanna Menardi, 2023. "Modal clustering of matrix-variate data," 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. 17(2), pages 323-345, June.
    11. Damjan Pfajfar & Emiliano Santoro, 2007. "Heterogeneity, Asymmetries and Learning in InfIation Expectation Formation: An Empirical Assessment," Money Macro and Finance (MMF) Research Group Conference 2006 123, Money Macro and Finance Research Group.
    12. Douglas L. Steinley, 2016. "Editorial," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 327-330, October.
    13. Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 249-282, September.
    14. Michael Windham & J. Hutchinson & Shizuhiko Nishisato & Ludovic Lebart & George Furnas & Richard Dubes & Frank Critchley & A. Gordon & Fionn Murtagh & Ulf Bockenholt & Philip Hopke & Daniel Wartenberg, 1988. "Book reviews," Journal of Classification, Springer;The Classification Society, vol. 5(1), pages 105-154, March.

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