Visual approach to supervised variable selection by self-organizing map.

Practical data analysis often encounters data sets with both relevant and useless variables. Supervised variable selection is the task of selecting the relevant variables based on some predefined criterion. We propose a robust method for this task.

Visualization of a pharmaceutical unit operation: wet granulation.

Recent developments in the field of process engineering and manufacturing sciences enable a new level of process understanding. However, extracting this understanding from increasing amounts of information is challenging. The aim of this study was to create a process vector from a model process describing all relevant information and, by that means, create a tool for combining and visualizing this information.