I was reading the Wikipedia page on k-medoids clustering and in the *Software* section, it’s stated that:

- Julia contains a k-medoid implementation of the k-means style algorithm (faster, but much worse result quality) in the JuliaStats/Clustering.jl package.

I don’t have experience doing k-medoids with anything other than Clustering.jl.

- Can anyone comment on this assertion?
- Do k-medoids implementations in R/Python produce “better quality” results?
- I haven’t really dug into the source code at Clustering.jl yet, so does anyone know if it implements the standard PAM algorithm (described here)?

If I get some time I might do some simple comparisons between the Clustering.jl implementation and whatever I can cook up with JuMP.jl. I think I should just be able to compare silhouette scores between the two results.

EDIT: they addressed this in the docs:

NoteThe function implements aK-means stylealgorithm instead ofPAM(Partitioning Around Medoids). K-means style algorithm converges in fewer iterations, but was shown to produce worse (10-20% higher total costs) results (see e.g. Schubert & Rousseeuw (2019)).