Self-organizing map

A self-organizing map is a type of artificial neural network that can be used to identify patterns in data. It is made up of input nodes, which represent the data that is being processed, and output nodes, which represent the different patterns or clusters in the data. The map uses a process called "unsupervised learning" which means that it looks at the data and automatically creates the patterns without any help from humans. The output nodes in the map are arranged in a grid that looks like a map. When the map is finished, it will show the different patterns in the data and where they appear in relation to each other.