What are some analysis functions which can be used on the K-Medoids algorithms?
My main aim is to compare results of 2 different clustering results in order to see which is better.
Can SSE (sum of squared errors) be applied to K-Medoids algorithm?
The original k-medoid publication discusses the measures ESS, along with several other measures such as average dissimilarity, maximum dissimilarity, diameter that may be more appropriate to use.
SSE is closely related to Euclidean distance, so it usually is not appropriate (unless, of course, you use Euclidean; but why would you use k-medoids then instead of k-means?)