GowerSom: Self-Organizing Maps for Mixed-Attribute Data Using Gower Distance

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Authors: Patricio Salas, Patricio Sáez
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References: 1

Abstract

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) .

Fields of Study & Subjects

Neural Networks and Applications Data Mining and Machine Learning Applications Statistical Methods and Applications

Publication details

Publication date
27 January 2026
Document type
Dataset