Self-Organizing Maps for Mixed-Attribute Data Using Gower Distance
Map observations to BMUs (Best Matching Units) using Gower distance
Predict BMUs for new data using a fitted Gower-SOM
Train a Gower-SOM on mixed-attribute data
Compute the U-Matrix for a trained Gower-SOM
Update categorical prototype in Gower-SOM (internal)
Plot the U-Matrix of a Gower-SOM
Implements a variant of the Self-Organizing Map (SOM) algorithm designed for mixed-attribute datasets. Similarity between observations is computed using the Gower distance, and categorical prototypes are updated via heuristic strategies (weighted mode and multinomial sampling). Provides functions for model fitting, mapping, visualization (U-Matrix and component planes), and evaluation, making SOM applicable to heterogeneous real-world data. For methodological details see Sáez and Salas (2026) <doi:10.1007/s41060-025-00941-6>.