I understood that PAM is just one kind of K-medoids algorithm. The difference is in new medoid selection (per iteration):
K-medoids selects object that is closest to the medoid as a next medoid
PAM tries out all of the objects in the cluster as a new medoid that will lead to lower SSE.
If I understood well, PAM gives better results, but takes up much more time. Is that so?
Which one is better and why?
Here is what confused me, this is a list of software that implements K-medoids, from Wikipedia
- ELKI includes several k-means variants, including k-medoids and PAM.
- Julia contains a k-medoid implementation in the Clustering package
- R includes in the "flexclust" package variants of k-means and in the "cluster" package.
- Gap An embrional open source library on distance based clustering.
- Java-ML. Includes a k-medoid implementation.
For example, it says that ELKI contains both variants, k-medoids and PAM?
And for example first look on K-medoids implementation in javaml looks like it finds the object closest to medoid and tries it out.