I am doing an unsupervised clustering analysis for a genomics project. This means that I do not know when a particular clustering analysis is good or not.
I am running different clustering algorithms and different 'sets of features'. What I mean with different 'sets of features' is that given a data frame, I choose different combination of columns depending on its biological importance. For instance, some variables measure things at the sequence level, while others are measuring a particular cellular process or some other feature that cannot be measured at the sequence level. I am playing around with the different outputs of these sets of features, running the algorithms with all the features, or ignoring some, etc .
What I want is to compare the different clusters of these different runs and see if some of my objects are being clustered similarly despite lacking some sets of features. Does this make sense?
Is there any recommendation on how can I do this?