SOM Algorithm for the Analysis of Multivariate Environmental Data

Analysis of multivariate environmental high frequency data by Self-Organizing Map and k-means clustering algorithms. By means of the graphical user interface it provides a comfortable way to elaborate by self-organizing map algorithm rather big datasets (txt files up to 100 MB ) obtained by environmental high-frequency monitoring by sensors/instruments. The functions present in the package are based on 'kohonen' and 'openair' packages implemented by functions embedding Vesanto et al. (2001) <> heuristic rules for map initialization parameters, k-means clustering algorithm and map features visualization. Cluster profiles visualization as well as graphs dedicated to the visualization of time-dependent variables Licen et al. (2020) are provided.


Reference manual

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1.1.2 by Sabina Licen, 6 months ago

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Authors: Sabina Licen [aut, cre] , Marco Franzon [aut] , Tommaso Rodani [aut] , Pierluigi Barbieri [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports rlist, kohonen, shiny, dplyr, plyr, openair, colourpicker, shinycssloaders, shinycustomloader

See at CRAN