I have a dataset with large number of features and about 15 000 observations. I’m using a probability distribution distance metric related to Jensen-Shannon divergence (JSD) to cluster the observations calculated as described in http://enterotype.embl.de/enterotypes.html. I’m applying R implementation of Partitioning Around Medoids (PAM) clustering to my JSD distance matrix.

The issue is that the size of the distance matrix seems to be too big and eats all the memory. I’m looking for alternative implementations. k-means doesn’t work with other distance metrics than eucleidean, R clara works only with eucleidean and manhattan distance matrices. Sparks do not support pam or clara yet.

• Will this proposal http://stats.stackexchange.com/a/12503/46598 work for transforming my JSD distance matrix to eucleidean distance matrix?
• What else would you consider?