SOM Algorithm: Self-Organizing Map Implementation in MATLAB

This article discusses the SOM algorithm and its MATLAB implementation as a classic textbook example program. The SOM (Self-Organizing Map) algorithm is a type of self-organizing neural network that performs unsupervised clustering and dimensionality reduction on data. The MATLAB implementation typically includes key functions for network initialization, competitive learning, and neighborhood updating using functions like maps, net, and trains. The algorithm employs a two-layer structure where input vectors are mapped to a topological grid through iterative weight adjustments. MATLAB serves as a powerful mathematical computing platform ideal for implementing SOM with built-in matrix operations and visualization tools. Beyond theoretical demonstrations, SOM algorithms in MATLAB can be applied to various fields including image processing, data mining, and artificial intelligence applications. Typical implementations involve defining network architecture parameters (grid size, learning rate), implementing the competitive learning process using Euclidean distance calculations, and visualizing results through U-matrix plots. Overall, the SOM algorithm and its MATLAB implementation represent valuable tools for enhanced data understanding and analysis through practical code examples.