I might be misunderstanding your point, but there's use cases that have repeatedly come up for me in multiple businesses, below being some examples, without getting too specific:
- identify latent features of customers via their behavioral data, to be used for profiling customers or recommending products to them
- within a large amount of customer behavioral data, identify potentially fraudulent behavior
- identify causes of seasonality (e.g. temporal patterns) in the data in order to improve forecasting (sales, traffic, whatever)
In those cases part of the investigation is to initially take a hands-off (unsupervised) approach, so that we can compare our initial top-down hypotheses with actual patterns in the data.
In both of those cases there's considerable (and sometimes adversarial) noise in the data.
Answers to these questions are actually Bayesian statistical models ("what is the probability of Y given a high likelihood of X"), treating these problems as unsupervised classification might work, but that's a very crude way of approaching them.
I wouldn't say it's a crude way of approaching the problem, it's a crude way of solving the problem. Taking the fraud example, taking unsupervised approaches to understanding patterns of the data before you impose assumptions on the data is a very useful process. For example, what might be fraudulent behaviors in the first place, assuming you aren't even sure you know what fraud looks like, or that it's actually all been detected? Your goal there might be to detect latent features period, not look at their predictive power for X.
Having understood that question, and built an understanding of what predicts fraud, you would then graduate to build models to understand the extent to which features predict fraudulence.
My point in context of the conversation is that it's useful in a business context to explore and understand that data.
- identify latent features of customers via their behavioral data, to be used for profiling customers or recommending products to them
- within a large amount of customer behavioral data, identify potentially fraudulent behavior
- identify causes of seasonality (e.g. temporal patterns) in the data in order to improve forecasting (sales, traffic, whatever)
In those cases part of the investigation is to initially take a hands-off (unsupervised) approach, so that we can compare our initial top-down hypotheses with actual patterns in the data.
In both of those cases there's considerable (and sometimes adversarial) noise in the data.