This is more a conceptual question than related to the implementation on decision trees.

I've a feature vector say V1,V2,V3,target_variable

If the vector is a,b,c,true then, using normal decision trees, we can classify the data set.

But if the variable V1 is a set say {x,y,z},b,c,true how can I implement it?

I think of methods by using x,y,z as dimensions rather than categories but the problem is the number of categories are very huge in the order if millions. This solution won't scale.

Are there any efficient ways to deal with this problem?