3.7.5.1 Unsupervised Training

In Unsupervised training, the network performance is tracked in a graph that shows the average match (confidence) of clustered input. Typically, the average match increases in a step-function. Each step indicates that the network has found a new cluster. Training can be stopped as soon as the average match has reached a stable situation. Usually this will be around 90%.

The colors of the input nodes in an unsupervised network will also change during training. In unsupervised mode, these colors do not indicate that one attribute is more important than another. All attributes in a clustering experiment are equally important.

Optionally, the neural network can be stored immediately on pressing OK. To do this, enter a neural network name in the appropriate field at the bottom of the NN training window.