Artificial Neural Networks – Self Organizing Map Working and Algorithm by Deeba Kannan
In this detailed lecture, I explained the working principle and algorithm of Self Organizing Maps (SOM) — a key concept in Artificial Neural Networks (ANN) and unsupervised learning. This session provides a step-by-step explanation of the Kohonen algorithm used to organize and cluster high-dimensional data.
Topics Covered:
What is a Self Organizing Map (SOM)?
Biological inspiration and Kohonen’s idea
Detailed explanation of SOM working process
SOM learning algorithm – steps and flow
Weight updates and neighborhood function
Convergence and map organization
Real-world applications of SOM in data visualization, clustering, and pattern recognition
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