# A Survey on Recent Advances in Self-Organizing Maps

arXiv: https://arxiv.org/abs/2501.08416 · HTML: https://mlsanity.com/paper/2501.08416/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Enhancing Math Learning in an LMS Using AI-Driven Question Recommendations](https://mlsanity.com/paper/2504.14098/) (2025)

## Abstract (arXiv)

Self-organising maps are a powerful tool for cluster analysis in a wide range of data contexts. From the pioneer work of Kohonen, many variants and improvements have been proposed. This review focuses on the last decade, in order to provide an overview of the main evolution of the seminal SOM algorithm as well as of the methodological developments that have been achieved in order to better fit to various application contexts and users' requirements. We also highlight a specific and important application field that is related to commercial use of SOM, which involves specific data management.