Self-Organizing Maps (SOM) 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, typically two-dimensional. This technique is useful for identifying patterns and relationships within complex datasets. SOMs work by adjusting the weights of neurons in response to input data, allowing similar data points to be grouped together. This process helps in tasks like data compression, feature extraction, and exploratory data analysis, making them valuable in fields such as data science, machine learning, and pattern recognition.