<ul><li>Many datasets contain hidden patterns and relationships that are challenging to identify and interpret using traditional techniques.</li><li>This essay demonstrates the practical application of Kohonen Self-Organizing Maps (SOMs) on a synthetic dataset, covering data preprocessing, hyperparameter tuning, and visualization.</li><li>The SOM successfully organized data into well-defined clusters, achieving a high classification accuracy (98.3%) with the K-Nearest Neighbors (KNN) model.</li><li>SOMs are effective for clustering and visualizing complex data, preserving topological relationships, and enabling accurate classification when combined with other models.</li></ul>

Mapping Hidden Patterns: A Practical Guide to Kohonen Self-Organizing Maps for Data Clustering and…

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