GowerSom (version 0.1.0)

gsom_Umatrix: Compute the U-Matrix for a trained Gower-SOM

Description

Calculates the U-Matrix (unified distance matrix) to visualize the topology and cluster structure of a Self-Organizing Map trained on mixed-attribute data. Each entry contains the average Gower distance between a neuron and its immediate neighbors in the rectangular grid.

Usage

gsom_Umatrix(codebook, n_rows, n_cols)

Value

A numeric matrix of size n_rows x n_cols, where each cell contains the average distance between the corresponding neuron and its neighbors.

Arguments

codebook

A data.frame or matrix containing the SOM prototypes (weights), with one row per neuron.

n_rows

Integer, number of rows in the SOM grid.

n_cols

Integer, number of columns in the SOM grid.

Author

Patricio Sáez <[e-mail]>; Patricio Salas <[e-mail]>

Details

The function assumes a rectangular topology where each neuron has up to four direct neighbors (up, down, left, right). For each neuron, the mean Gower distance to its valid neighbors is computed using daisy with metric = "gower".

References

Sáez, P., Salas, P. Gower-SOM: a self-organizing map for mixed data with gower distance and heuristic adaptation for data analytics. Int J Data Sci Anal 22, 26 (2026). https://doi.org/10.1007/s41060-025-00941-6/."

See Also

daisy

Examples

set.seed(1)
df <- data.frame(
  x1 = rnorm(20),
  x2 = rnorm(20),
  g  = factor(sample(letters[1:3], 20, TRUE))
)
fit <- gsom_Training(df, grid_rows = 3, grid_cols = 3,
                num_iterations = 5, batch_size = 4)
U <- gsom_Umatrix(fit$weights, n_rows = 3, n_cols = 3)
plot_Umatrix(U)