MATLAB Implementation of Self-Organizing Map (SOM) Algorithm

This document presents the Self-Organizing Map (SOM) algorithm and its MATLAB implementation, which serve as classic textbook examples in computational intelligence. The SOM algorithm, implemented through MATLAB's neural network toolbox or custom code, utilizes competitive learning and neighborhood functions to create topological mappings of high-dimensional data. This algorithm finds extensive applications in data mining, image processing, and artificial intelligence domains. MATLAB, as a widely adopted computational software platform, provides specialized functions like "selforgmap" and "newsom" for SOM implementation, along with visualization tools for analyzing the resulting feature maps. When combining these technologies, MATLAB enables efficient SOM programming for diverse applications - for instance, using SOM's clustering capabilities for pattern recognition in image processing through matrix operations and distance calculations, or leveraging its classification features for identifying investment opportunities in finance via data normalization and weight update mechanisms. The implementation typically involves initializing neuron weights, calculating Euclidean distances, identifying winning neurons (Best Matching Units), and updating weights using neighborhood functions. Therefore, proficiency in these tools and techniques, including understanding code structure, parameter tuning, and result interpretation, remains crucial for professionals across various industries working with unsupervised learning and dimensionality reduction tasks.