Model-based co-clustering for mixed type data
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DOI: 10.1016/j.csda.2019.106866
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References listed on IDEAS
- Matthieu Marbac & Christophe Biernacki & Vincent Vandewalle, 2017. "Model-based clustering of Gaussian copulas for mixed data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(23), pages 11635-11656, December.
- Damien McParland & Isobel Claire Gormley, 2016. "Model based clustering for mixed data: clustMD," 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. 10(2), pages 155-169, June.
- 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.
- Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
- Jacques, Julien & Biernacki, Christophe, 2018. "Model-based co-clustering for ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 101-115.
- Margot Selosse & Julien Jacques & Christophe Biernacki & Florence Cousson‐Gélie, 2019. "Analysing a quality‐of‐life survey by using a coclustering model for ordinal data and some dynamic implications," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(5), pages 1327-1349, November.
- Christophe Biernacki & Alexandre Lourme, 2019. "Unifying data units and models in (co-)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. 13(1), pages 7-31, March.
- Charles Bouveyron & Laurent Bozzi & Julien Jacques & François‐Xavier Jollois, 2018. "The functional latent block model for the co‐clustering of electricity consumption curves," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(4), pages 897-915, August.
- Bhatia, Parmeet Singh & Iovleff, Serge & Govaert, Gérard, 2017. "blockcluster: An R Package for Model-Based Co-Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i09).
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Cited by:
- C. Biernacki & J. Jacques & C. Keribin, 2023. "A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges," Journal of Classification, Springer;The Classification Society, vol. 40(2), pages 332-381, July.
- Alessandro Casa & Charles Bouveyron & Elena Erosheva & Giovanna Menardi, 2021. "Co-clustering of Time-Dependent Data via the Shape Invariant Model," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 626-649, October.
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Keywords
Co-clustering; Mixed-type data; Latent block model;All these keywords.
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