Multi-Partitions Subspace Clustering.
Vandewalle, V.
Mathematics, 8(4): 597. 2020.
link
bibtex
@article{vandewalle2020multi,
Author = {Vandewalle, Vincent},
Date-Added = {2021-02-05 10:50:58 +0100},
Date-Modified = {2021-02-05 10:50:58 +0100},
Journal = {Mathematics},
Keyword = {VVmulti},
Number = {4},
Pages = {597},
Publisher = {Multidisciplinary Digital Publishing Institute},
Title = {Multi-Partitions Subspace Clustering},
Volume = {8},
Year = {2020}}
Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering.
Biernacki, C.; Marbac, M.; and Vandewalle, V.
Journal of Classification. 2020.
![Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering [link]](//bibbase.org/img/filetypes/link.svg "Paper")
Paper
doi
link
bibtex
abstract
@article{biernacki2020gaussian,
Abstract = {A generic method is introduced to visualize in a ``Gaussian-like way,''and onto ℝ2{\$}{$\backslash$}mathbb {\{}R{\}}\^{}{\{}2{\}}{\$}, results of Gaussian or non-Gaussian--based clustering. The key point is to explicitly force a visualization based on a spherical Gaussian mixture to inherit from the within cluster overlap that is present in the initial clustering mixture. The result is a particularly user-friendly drawing of the clusters, providing any practitioner with an overview of the potentially complex clustering result. An entropic measure provides information about the quality of the drawn overlap compared with the true one in the initial space. The proposed method is illustrated on four real data sets of different types (categorical, mixed, functional, and network) and is implemented on the r package ClusVis.},
Author = {Biernacki, Christophe and Marbac, Matthieu and Vandewalle, Vincent},
Da = {2020/07/11},
Date-Added = {2021-02-05 10:50:58 +0100},
Date-Modified = {2021-02-05 10:50:58 +0100},
Doi = {10.1007/s00357-020-09369-y},
Id = {Biernacki2020},
Journal = {Journal of Classification},
Title = {Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering},
Ty = {JOUR},
Url = {https://doi.org/10.1007/s00357-020-09369-y},
Year = {2020},
Bdsk-Url-1 = {https://doi.org/10.1007/s00357-020-09369-y}}
A generic method is introduced to visualize in a Gaussian-like way,''and onto ℝ2$}{$$}mathbb {\{}R{\}}^{\{}2{\}}{$, results of Gaussian or non-Gaussian–based clustering. The key point is to explicitly force a visualization based on a spherical Gaussian mixture to inherit from the within cluster overlap that is present in the initial clustering mixture. The result is a particularly user-friendly drawing of the clusters, providing any practitioner with an overview of the potentially complex clustering result. An entropic measure provides information about the quality of the drawn overlap compared with the true one in the initial space. The proposed method is illustrated on four real data sets of different types (categorical, mixed, functional, and network) and is implemented on the r package ClusVis.
Estimating the number of usability problems affecting medical devices: modelling the discovery matrix.
Vandewalle, V.; Caron, A.; Delettrez, C.; Périchon, R.; Pelayo, S.; Duhamel, A.; and Dervaux, B.
BMC Medical Research Methodology, 20(234). 2020.
link
bibtex
@article{vandewalle2020estimating,
Author = {Vandewalle, Vincent and Caron, Alexandre and Delettrez, Coralie and P{\'e}richon, Renaud and Pelayo, Sylvia and Duhamel, Alain and Dervaux, Benoit},
Date-Added = {2021-02-05 10:50:58 +0100},
Date-Modified = {2021-02-05 10:50:58 +0100},
Journal = {BMC Medical Research Methodology},
Keyword = {VVusability},
Number = {234},
Title = {Estimating the number of usability problems affecting medical devices: modelling the discovery matrix},
Volume = {20},
Year = {2020}}
Clustering spatial functional data.
Vandewalle, V.; Preda, C.; and Dabo, S.
In Mateu, J.; and Giraldo, R., editor(s),
Geostatistical Functional Data Analysis : Theory and Methods. John Wiley and Sons, Chichester, UK, 2020.
ISBN: 978-1-119-38784-8
link
bibtex
@incollection{dabo2018geostatistical,
Address = {Chichester, UK},
Author = {Vandewalle, Vincent and Preda, Cristian and Dabo, Sophie},
Booktitle = {Geostatistical Functional Data Analysis : Theory and Methods},
Date-Added = {2021-02-05 10:50:58 +0100},
Date-Modified = {2021-02-05 10:50:58 +0100},
Editor = {J. Mateu and R. Giraldo},
Keyword = {VVfun},
Note = {{ISBN:} 978-1-119-38784-8},
Publisher = {John Wiley and Sons},
Title = {Clustering spatial functional data},
Year = {2020}}
cfda: an R Package for Categorical Functional Data Analysis.
Preda, C.; Grimonprez, Q.; and Vandewalle, V.
October 2020.
working paper or preprint
Paper
link
bibtex
@unpublished{preda:hal-02973094,
Author = {Preda, Cristian and Grimonprez, Quentin and Vandewalle, Vincent},
Date-Added = {2021-02-05 10:50:58 +0100},
Date-Modified = {2021-02-05 10:50:58 +0100},
Hal_Id = {hal-02973094},
Hal_Version = {v1},
Keywords = {functional data ; categorical data ; stochastic process ; multiple correspondence analysis},
Month = oct,
Note = {working paper or preprint},
Pdf = {https://hal.inria.fr/hal-02973094/file/CFDA-2.pdf},
Title = {{cfda: an R Package for Categorical Functional Data Analysis}},
Url = {https://hal.inria.fr/hal-02973094},
Year = 2020,
Bdsk-Url-1 = {https://hal.inria.fr/hal-02973094}}
![Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering [link]](http://bibbase.org/img/filetypes/link.svg "Paper")