I am doing some web page clustering work and I'm going to use cosine similarity as my distance measure. Even though cosine similarity is a clustering technique, I have to give training data in order to build the query vector. Clustering algorithm doesn't need training data in the sense of with labeled classes, but how do you build the query vector if you don't give the training data in the cosine similarity calculation?
I am only interested in a single topic (sports) so if I do it with 2 clusters, when a new document is fed, if it is clustered to the cluster 1 (say sports), then I'll take that document or else it will be rejected. In this case how this differs from single-class classification?