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Two-mode multi-partitioning

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  • Rocci, Roberto
  • Vichi, Maurizio

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  • Rocci, Roberto & Vichi, Maurizio, 2008. "Two-mode multi-partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1984-2003, January.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:4:p:1984-2003
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    1. M. Bittner & P. Meltzer & Y. Chen & Y. Jiang & E. Seftor & M. Hendrix & M. Radmacher & R. Simon & Z. Yakhini & A. Ben-Dor & N. Sampas & E. Dougherty & E. Wang & F. Marincola & C. Gooden & J. Lueders &, 2000. "Molecular classification of cutaneous malignant melanoma by gene expression profiling," Nature, Nature, vol. 406(6795), pages 536-540, August.
    2. Wayne Desarbo, 1982. "Gennclus: New models for general nonhierarchical clustering analysis," Psychometrika, Springer;The Psychometric Society, vol. 47(4), pages 449-475, December.
    3. Bock, Hans-Hermann & Vichi, Maurizio, 2007. "Statistical Learning Methods Including Dimensionality Reduction," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 370-373, September.
    4. Robert M. Pitti & Scot A. Marsters & David A. Lawrence & Margaret Roy & Frank C. Kischkel & Patrick Dowd & Arthur Huang & Christopher J. Donahue & Steven W. Sherwood & Daryl T. Baldwin & Paul J. Godow, 1998. "Genomic amplification of a decoy receptor for Fas ligand in lung and colon cancer," Nature, Nature, vol. 396(6712), pages 699-703, December.
    5. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    6. Celeux, Gilles & Govaert, Gerard, 1992. "A classification EM algorithm for clustering and two stochastic versions," Computational Statistics & Data Analysis, Elsevier, vol. 14(3), pages 315-332, October.
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    1. Fernández, D. & Arnold, R. & Pledger, S., 2016. "Mixture-based clustering for the ordered stereotype model," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 46-75.
    2. Atsuho Nakayama & Daniel Baier, 2020. "Predicting brand confusion in imagery markets based on deep learning of visual advertisement content," 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. 14(4), pages 927-945, December.
    3. Eleni Matechou & Ivy Liu & Daniel Fernández & Miguel Farias & Bergljot Gjelsvik, 2016. "Biclustering Models for Two-Mode Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 611-624, September.
    4. Daniel Baier & Sarah Frost, 2018. "Relating brand confusion to ad similarities and brand strengths through image data analysis and classification," 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. 12(1), pages 155-171, March.
    5. Laura Bocci & Donatella Vicari, 2019. "ROOTCLUS: Searching for “ROOT CLUSters” in Three-Way Proximity Data," Psychometrika, Springer;The Psychometric Society, vol. 84(4), pages 941-985, December.
    6. Daniel Fernández & Radim J. Sram & Miroslav Dostal & Anna Pastorkova & Hans Gmuender & Hyunok Choi, 2018. "Modeling Unobserved Heterogeneity in Susceptibility to Ambient Benzo[ a ]pyrene Concentration among Children with Allergic Asthma Using an Unsupervised Learning Algorithm," IJERPH, MDPI, vol. 15(1), pages 1-18, January.
    7. Francesca Martella & Maurizio Vichi, 2012. "Clustering microarray data using model-based double K -means," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(9), pages 1853-1869, April.
    8. Mansour Zarrin & Jan Schoenfelder & Jens O. Brunner, 2022. "Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework," Health Care Management Science, Springer, vol. 25(3), pages 406-425, September.
    9. Carlo Cavicchia & Maurizio Vichi & Giorgia Zaccaria, 2022. "Gaussian mixture model with an extended ultrametric covariance structure," 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(2), pages 399-427, June.
    10. Gérard Govaert & Mohamed Nadif, 2018. "Mutual information, phi-squared and model-based co-clustering for contingency tables," 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. 12(3), pages 455-488, September.
    11. Álvarez de Toledo, Pablo & Núñez, Fernando & Usabiaga, Carlos, 2018. "Matching and clustering in square contingency tables. Who matches with whom in the Spanish labour market," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 135-159.
    12. J. Fernando Vera & Rodrigo Macías, 2017. "Variance-Based Cluster Selection Criteria in a K-Means Framework for One-Mode Dissimilarity Data," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 275-294, June.
    13. Aurore Lomet & Gérard Govaert & Yves Grandvalet, 2018. "Model selection for Gaussian latent block clustering with the integrated classification likelihood," 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. 12(3), pages 489-508, September.
    14. Daniel Fernández & Richard Arnold & Shirley Pledger & Ivy Liu & Roy Costilla, 2019. "Finite mixture biclustering of discrete type multivariate 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. 13(1), pages 117-143, March.
    15. Adelaide Freitas & Eloísa Macedo & Maurizio Vichi, 2021. "An empirical comparison of two approaches for CDPCA in high-dimensional data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 1007-1031, September.
    16. J. Vera & Rodrigo Macías & Willem Heiser, 2013. "Cluster Differences Unfolding for Two-Way Two-Mode Preference Rating Data," Journal of Classification, Springer;The Classification Society, vol. 30(3), pages 370-396, October.

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