A Survey on Recent Advances in Self-Organizing Maps

TL;DR

This survey reviews recent developments in self-organizing maps over the past decade, highlighting algorithmic evolutions, methodological improvements, and applications in commercial data management contexts.

Contribution

It provides a comprehensive overview of the latest advances in SOM algorithms and their adaptations for diverse applications and user needs.

Findings

Significant algorithmic improvements have been made in SOM over the last decade.

Methodological developments have enhanced SOM's applicability to various data types.

SOM has been effectively applied in commercial data management scenarios.

Abstract

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.