But this is not about clustering. It's about figuring out to what extent a certain subclass of features, namely the 'shapelets', are statistically significantly

But this is not about clustering. It's about figuring out to what extent a certain subclass of features, namely the 'shapelets', are statistically significantly associated with a pre-defined binary outcome. The paper you mentioned is interesting, though, because it shows an issue that many algorithms are privy to: if the number of samples/features gets too large, at some point, you are only comparing _means_. (We are working on a paper to show the issues of this when it comes to time series classification.)