Self-organizing Maps (SOMs) are a type of unsupervised neural network that uses competitive learning to produce a low-dimensional, discretized representation of input data, preserving the topological properties of the input space. They are particularly useful for visualizing high-dimensional data and clustering tasks, often applied in fields like data mining and pattern recognition.