In the above maps you see the intensity of missing dots on printed paper (right side) and the values of a special parameter of the printing process (left). One sees that there is a region of correlation in the left/down corner but the many-missing-dots region in the center has no correspondence at this parameter (it might at another process parameter). One could imagine to have a monitoring board where all process parameters and quality parameters are represented as SOM und you control your process by staying in the high-quality regions of your quality parameters.
I Saw It With My Eyes
Our machine learning framework is a multi-method-multi-strategy system. One of the methods: self organizing maps (SOM). A SOM is a type of artificial neural network that is trained using unsupervised learning to produce a, say, two-dimensional map from a usually high-dimensional input space of the training samples. They use a neighbourhood function to preserve the topological properties of the input space. This makes SOM useful for visualizing low-dimensional views of high-dimensional parameter spaces.