DiatSOM: A R-package for diatom biotypology using self-organizing maps

Marius Bottin, Jean Luc Giraudel, Sovan Lek, Juliette Tison-Rosebery

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

9 Citas (Scopus)

Resumen

Owing to the high complexity of diatom community data, there is a special need for methods accounting for complex non-linear gradients. A Kohonen's self-organizing map (SOM) is a neural network with unsupervised learning. It allows both unbiased classification of the communities and visualization of biological gradients on a two-dimensional plane. However, as with other neural networks, many parameters must be set. A new R-package with a SOM parameterization specifically suited to diatom communities has been developed. Further developments will consist of creating a graphical user interface in order to make this method easier to use for the scientific community.

Idioma originalInglés estadounidense
Páginas (desde-hasta)5-9
Número de páginas5
PublicaciónDiatom Research
Volumen29
N.º1
DOI
EstadoPublicada - ene 2 2014
Publicado de forma externa

All Science Journal Classification (ASJC) codes

  • Ciencias acuáticas

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