Training Self-Organizing Map (SOM) Neural Networks with MATLAB Implementation

This is a MATLAB-implemented source code for training Self-Organizing Map (SOM) neural networks. The Self-Organizing Map is an unsupervised learning algorithm primarily used for data clustering and feature extraction. It helps reveal relationships within data and identifies hidden patterns and structures. The implementation includes key MATLAB functions such as newsom for network initialization, trains for competitive learning training, and plotsom for visualizing topological maps. Using this codebase, you can efficiently train custom SOM networks and apply them to various domains including image processing, speech recognition, and data mining. The code structure supports parameter configuration for learning rates, neighborhood functions, and grid dimensions. Whether you're a beginner or experienced developer, this implementation provides a robust framework to explore data potential through neuron weight adaptation and topological preservation mechanisms.