[motiwari]

One thing that's important to note is that k-medoids supports arbitrary distance metrics -- in fact, your dissimilarity measure need not even be a metric (it can be negative, asymmetric, not satisfy the triangle inequality, etc.)

An implication of this is that if you were to do some invertible data transformation and then perform clustering, that's equivalent to doing clustering with a different dissimilarity measure (without the data transformation in the first place). It should be possible to avoid doing the invertible data transformation in the first place if you're willing to engineer your dissimilarity measure.

Without more details, it's hard to say exactly what would happen to the clustering results under custom dissimilarity measures or data transformations -- but our package supports both use cases!