PCA before Affinity Propagation (AP) I have a large-ish dataset (100k samples, ~100 features), that I am trying to cluster, to an unknown number of clusters.
I thought of using PCA first, to reduce dimensionality, since I understand that distances (euclidean, cosine) in high-dimensionality might not be actually accurate.

However, I found very little on using PCA before AP, unlike K-Means, which made me wonder - is there any reason to not use PCA and then affinity propagation?
Independent of the number of features, you will obviously need much more than

3 * 100.000 * 100.000 * 8 bytes

of ram (with double precision floats). That is 240.000.000.000 or about   240 GB. Not only is this a lot of RAM to have, but AP will have to do many passes over this RAM, so this will take forever, even after computing the distance matrix.

So clearly PCA won't help. You just cannot afford an algorithm with O(n²) memory and O(n²) runtime. PCA reduces d, but the runtime of AP itself does not depend on d.