> Feature agglomeration uses an Euclidean metric so it's very unlikely to work
> for those cases.

Well, in theory, you can make it use the metric that you want, now that
there is complete linkage and average linkage. In practice it would
probably be hard to use. That said, the impact of metrics is way oversold
in my experience. It was very hard for me to create an example that would
show in a clear-cut way the usefulness of choosing different metrics.
Also, keep in mind that the difference between l_p metrics is bounded.

> Unless the features are normalized in some way using StandardScaler or
> a boxcox transform for instance?

Yes, standard scaler or boxcox transform would probably be very useful
(we don't have a boxcox transformer in scikit-learn, it would be great to
have one).

> I thought it would be worth mentioning this expected limitation in the doc.

Will do.