Classification Algorithm Based on Kohonen Network

The Kohonen Network is a self-organizing competitive neural network capable of autonomously identifying environmental features and performing clustering. Through self-organizing feature mapping, the network adjusts its weights to converge into a representation where each neuron becomes highly responsive to a specific input pattern. This unsupervised learning network was introduced by Professor Teuvo Kohonen from the University of Helsinki, Finland. The learning process involves unsupervised self-organization, where neurons engage in competitive learning to specialize in different input patterns, allowing specific neurons to function as detectors for particular input patterns in recognition tasks. After training, neurons are organized into distinct regions, each demonstrating unique response characteristics to input models. Beyond these fundamentals, Kohonen Networks find applications in various domains such as image processing, data mining, and pattern recognition. In practice, implementing a Kohonen network involves initializing weight vectors randomly, calculating Euclidean distances between input vectors and weights, selecting the winning neuron (Best Matching Unit) with the smallest distance, and updating weights within a neighborhood function using a learning rate. Additionally, the network enhances data visualization by projecting high-dimensional data onto a 2D topological map while preserving inherent relationships.