If I have a dataset that is labeled with positive and negative examples, and I'd like to cluster (i.e. unsupervised) only the positive examples, does it make sense to reduce dimensionality using feature selection methods (which require data to be labeled, i.e. supervised) before performing clustering on the data?
For example: I have data about a variety of people that either buy gum or not. I want to know what the different types of gum buyers might look like, hence I only want to cluster the positive examples. However, since I have so many attributes for each person, I want to reduce the number of features I use before clustering them. Does it make sense to use this classification to select features if I am only clustering the positive examples? What pitfalls am I not seeing?