Kohonen networks are a type of neural network that perform clustering, also known as a knet, or a self-organizing map. This type of network can be used to cluster the data set into distinct groups when you don't know what those groups are at the beginning. Records are grouped so that records within a group or cluster tend to be similar to each other, and records in different groups are dissimilar.
The basic units are neurons, and they are organized into two layers: the input layer and the output layer (also called the output map). All of the input neurons are connected to all of the output neurons, and these connections have strengths, or weights, associated with them. During training, each unit competes with all of the others to "win" each record.
The output map is a two-dimensional grid of neurons, with no connections between the units.
Input data is presented to the input layer, and the values are propagated to the output layer. The output neuron with the strongest response is said to be the winner and is the answer for that input.
Initially, all weights are random. When a unit wins a record, its weights (along with those of other nearby units, collectively referred to as a neighborhood) are adjusted to better match the pattern of predictor values for that record. All of the input records are shown, and weights are updated accordingly. This process is repeated many times until the changes become very small. As training proceeds, the weights on the grid units are adjusted so that they form a two-dimensional "map" of the clusters (hence the term self-organizing map).
When the network is fully trained, records that are similar should appear close together on the output map, whereas records that are vastly different will appear far apart.