Lots of people use cluster analysis. I've heard very few explicitly say why. I imagine this is because within a given field, most researchers seem to understand why clustering is used for the problems typical to that area - but uses vary between fields, and I haven't seen those differences addressed anywhere.
I am particularly interested in the contrast between latent-variable interpretations of model-based methods (mixture models), and clustering applications in machine learning that don't seem as worried about interpretation of clusters, just that they are useful in some way. Data reduction is an even more agnostic application that is very common.
There are loads of papers comparing different methods for clustering - but I can't find any that compare philisophical/theoretical approaches. If you know of any, could you please list them here?