Self-Organizing Maps (SOMs) are a type of artificial neural network used for unsupervised learning. They help visualize and cluster high-dimensional data by mapping it onto a lower-dimensional grid. Each node in the grid represents a group of similar data points, making it easier to identify patterns and relationships within the data. SOMs are particularly useful in fields like data mining and image processing. By organizing complex datasets into a simpler format, they allow researchers and analysts to gain insights and make informed decisions based on the underlying structure of the data.