Comprehensive Guide to Using Self-Organizing Maps (SOM) in MATLAB with Practical Implementation

This comprehensive guide illustrates the implementation of Self-Organizing Maps (SOM) in MATLAB, providing detailed annotations at each step accompanied by illustrative diagrams to facilitate quick learning and thorough mastery. The tutorial begins by explaining fundamental SOM concepts and principles, including neural network architecture and self-organizing characteristics. Subsequently, we elaborate on the implementation workflow covering critical phases such as data preprocessing using MATLAB's normalization functions (e.g., mapminmax), weight initialization strategies via rand or normr functions, and neighborhood function definitions through Gaussian or bubble functions using the newsom function. Practical code demonstrations include complete MATLAB script examples showing SOM training with the learnsomb function, topology visualization via plotsom, and hit analysis using plotsomhits. Additionally, we address common implementation challenges and present real-world application scenarios with accompanying MATLAB code snippets to enhance practical application skills. This resource enables readers to develop comprehensive SOM understanding and apply the technique flexibly in diverse computational scenarios. Key implementation aspects covered: - Data normalization using mapstd or zscore functions - SOM network creation with newsom specifying dimensions and cover steps - Training configuration setting learning rates and neighborhood radii - Visualization techniques for weight positions and sample distributions - Performance evaluation through quantization error analysis