I'm faced with a data set where one of the features is a set of 4-5 categories (this number of categories isn't constant). I need to use this feature for building a decision tree. I searched online for any clustering techniques where this could work, to no avail.
One approach could be to use columns for each of the categories and set 1 or 0 depending on whether that category appears in the set or not, but since the total number of categories is very large ( ~ 2000) it is not a viable option.
Moreover, I'm interested in finding the rules, more so than building an accurate model. So the final rules obtained from the decision tree should be such that we can interpret them in terms of the categories.
Any help would be appreciated.