SOM Algorithm for the Analysis of Multivariate Environmental Data
BMUs of the cluster centroids
Cluster assignment for the experimental data
Boxplot of prototype variables split by cluster and variable
Boxplot of prototype variables split by cluster
Custom color sequence for clusters
Prototype coordinates for graph
Plot of daily percentages for each cluster
Evaluate Davis-Bouldin index for the cluster split of data input
Percentage frequency for each cluster
Daily percentage frequency for each cluster
Monthly percentage frequency for each cluster
Function to draw an hexagon around a point
Function to draw an hexagonal SOM map
SOM map with clusters
Heatmaps
Hits distribution on the SOM map
Hits distribution on the SOM map
Relative quantization error distribution on the SOM map
Realtive quantization error distribution on the SOM map
K-means algorithm applied for different values of clusters
Custom number sequence for clusters
Basic statistics of values present in the input vector
Calculate map dimensions
Calculate initialization matrix for SOM training
K-means algorithm applied for a specific number of clusters
Evaluate pairwise distance matrix for the given codebook
Unified distance matrix for the SOM map
The function starts the SOMEnv GUI
Topographical error for the SOM map
U-matrix plot
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) <http://www.cis.hut.fi/projects/somtoolbox/package/papers/techrep.pdf> 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) <doi:10.4209/aaqr.2019.08.0414> are provided.