Understanding and Implementing Self-Organizing Maps (SOM) with Python

Introduction

In the rapidly evolving world of machine learning, Self-Organizing Maps (SOMs) stand out as a fascinating and powerful tool for clustering and visualizing high-dimensional data. This blog post dives into the science and mathematics behind SOMs and provides a comprehensive Python implementation using the MiniSom library.

What is a Self-Organizing Map?

A Self-Organizing Map (SOM) is a type of unsupervised neural network introduced by Teuvo Kohonen in the 1980s. Unlike traditional neural networks that are typically used for classification or regression, SOMs are used for clustering and visualizing high-dimensional data by mapping it onto a lower-dimensional (usually 2D) grid. The primary goal of SOMs is to preserve the topological properties of the input space, meaning that similar data points in the high-dimensional space remain close to each other in the lower-dimensional map.

Key Characteristics of SOMs

• Unsupervised Learning: SOMs learn patterns in the data without needing labeled examples.
• Topology Preservation: The spatial arrangement of neurons in the SOM corresponds to the similarities in the input data.