A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges

Autor: Biernacki, Christophe, Jacques, Julien, Keribin, C.

Model-based co-clustering can be seen as a particularly valuable extension of model-based clustering for three main reasons: (1) while allowing parsimoniously a drastic reduction of both the number of lines/individuals and columns/variables of a data

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::37d0d6cb0d1312f713a67a78ab7e82cb
https://hal.science/hal-03769727

Model-based Cluster and Discriminant Analysis with the MIXMOD software

Autor: Florent Langrognet, Christophe Biernacki, Gérard Govaert, Gilles Celeux

Publikováno v: Computational Statistics and Data Analysis
Computational Statistics and Data Analysis, Elsevier, 2006, 51, pp.587-600
Computational Statistics and Data Analysis, 2006, 51, pp.587-600

International audience; The Mixture Modeling (MIXMOD) program fits mixture models to a given data set for the purposes of density estimation, clustering or discriminant analysis. A large variety of algorithms to estimate the mixture parameters are pr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93baf584788c46766e939816b8a4b891
https://hal.archives-ouvertes.fr/hal-00469477

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