A self-organizing map (SOM), also known as a Kohonen map, is a type of neural network used in unsupervised learning. It is inspired by the way the human brain organizes information and can be used for tasks such as clustering, dimensionality reduction, and visualization.
The SOM consists of an input layer and an output layer. The input layer contains the data to be analyzed, and the output layer consists of a grid of nodes, each of which represents a different region of the input space. The nodes are connected to each other and to the input layer by weighted connections.
During training, the SOM adjusts the weights of the connections between the nodes and the input layer in order to cluster the data in a way that preserves the topology of the input space. The nodes that are closest to a given input become activated, while the other nodes remain inactive. As a result, the SOM produces a low-dimensional representation of the high-dimensional input data, where similar inputs are mapped to nearby nodes on the output grid.
SOMs have been used in a variety of applications, such as image analysis, speech recognition, and anomaly detection. They are particularly useful for visualizing high-dimensional data and discovering patterns that are not easily detected using other methods.