Self-Organizing Maps (SOM)

Self-Organizing Maps (SOM) are a type of neural network that helps visualize and organize complex data by mapping it onto a two-dimensional grid. They analyze patterns and relationships in high-dimensional data, then group similar data points together on the map. Over time, the SOM adjusts itself to represent the underlying structure, making it easier to identify clusters, trends, and insights. It's like creating a visual “landscape” of data, revealing connections and structures that might be hard to see otherwise. SOMs are useful for tasks like data visualization, pattern recognition, and reducing complex information into understandable formats.