Many analytics algorithms only accept numerical values, for example distance based algorithms. In the cases then when a nominal data column is present, the analyst needs to implement a few tricks to include the data column into the analysis.
One trick is of course to assign numbers to (to encode) the different nominal values. Another trick is to create a binary matrix with the same nominal values.
Let's make an example of such a binary matrix. Let's read the adult.data file. This data set contains a string field "native-country". We want to transform a data column with a few nominal values, like "United States", "France", "Canada", and so on, into a matrix with column headers provided by the nominal values of the column and values 0/1 depending on whether the record/person was born in that country or not (see table below).
Applying the "One2Many" node to the "native-country" column of the adult data set, we got the following data representation., where "United States" is translated in "1" under column "United States" and "0" under all other native-country related columns.