This is more of a theoretical/solving an argument sort of question.
Assuming I have a bunch of data point with 11 features I consider relevant about each point and 2 "labels": one is a boolean label ( 0 or 1), one is a continuous "label" (thought I'm not sure the word label really applies here).
What I want to do is basically: Find a way to predict both of these labels as accurately as possible. Find the features which influence these labels the most (even if by finding said features I'm unable to find a strong equation for actually predicting the labels).
Also, of note would be the fact that, although I care about both labels, predicting the second would automatically mean I predict the first and predicting the first is enough to "solve" the problem in most cases. With me until now? Ok, perfect.
Someone suggested to me that this would be a perfect scenario to apply clustering algorithms, but I'm not quite sure why. I was under the impression that clustering, generally speaking, is used to label unlabeled data (I have labeled data here) and for actually making predictions on unlabeled data a regression (be it with a SVM, a NN or w/e) or classification type algorithm is what can be used.
The only situation in which I could see clustering being used here is to turn the continuous label into a fixed number of labels.
Is there another situation in which a clustering algorithm can be applied to this sort of problem? Are there ways to use clustering algorithms to predict already existing labels or figure out if there is even a relation at all between a given label and a feature? Can clustering algorithms be used to filter out irrelevant features?