Greedy clustering of count data through a mixture of multinomial PCA

Autor: Nicolas Jouvin, Charles Bouveyron, Pierre Latouche, Alain Livartowski, Guillaume Bataillon

Publikováno v: Computational Statistics
Computational Statistics, Springer Verlag, In press, ⟨10.1007/s00180-020-01008-9⟩
Computational Statistics, In press, ⟨10.1007/s00180-020-01008-9⟩

Count data is becoming more and more ubiquitous in a wide range of applications, with datasets growing both in size and in dimension. In this context, an increasing amount of work is dedicated to the construction of statistical models directly accoun

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b2e9b93504fcb0d25b3d72ee03d85dc
https://doi.org/10.1007/s00180-020-01008-9

Hierarchical clustering with discrete latent variable models and the integrated classification likelihood

Autor: Etienne Côme, Nicolas Jouvin, Charles Bouveyron, Pierre Latouche

Publikováno v: Advances in Data Analysis and Classification
Advances in Data Analysis and Classification, Springer Verlag, In press, ⟨10.1007/s11634-021-00440-z⟩
Advances in Data Analysis and Classification, 2021, 15, pp.957-986. ⟨10.1007/s11634-021-00440-z⟩

Finding a set of nested partitions of a dataset is useful to uncover relevant structure at different scales, and is often dealt with a data-dependent methodology. In this paper, we introduce a general two-step methodology for model-based hierarchical

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3aaac0317b0877e0de7aa32bf54ac42
https://hal.archives-ouvertes.fr/hal-02530705