"Dealing with nulls in FP Growth"
I am very new to using rapid miner, and have a question about the process in which variables with many levels are translated into binomial variables through FP Growth.
For example, in a survey's data I am trying to analyze, one of our variables is age groups. They go 0-13, 13-18, 19-24, 25-29, etc. I understand that it will create a variable for all of these groups (except one) and assign 1's and 0's for whether it falls into the group or not.
My problem is when a participant did not answer the age question. How does Rapid Miner handle these observations? Will it assign a zero in each of the categories, or eliminate it completely? I believe it would mess up the results if the former and thus would prefer that people who did not answer the age question not be considered when trying to discover rules involving age.
Is this how Rapid Miner already works, and if not, is there some way to set the process up to do this?
Thank you in advance.