The streaming k-means works by building a sketch of the data which is then used to do real clustering.
It might be that this sketch would be acceptable to do k-medoids, but that is definitely not guaranteed. Similarly, it might be possible to build a medoid sketch instead of a mean based sketch, but this is also unexplored ground. The virtue of the first approach (using a m-means sketch as input to k-medoids) would be that it would make the k-medoids scalable. On Mon, Jun 1, 2015 at 1:04 PM, Marko Dinic <marko.di...@nissatech.com> wrote: > Hello everyone, > > I have an idea and I would like to get a validation from community about > it. > > In Mahout there is an implementation of Streaming K-means. I'm interested > in your opinion would it make sense to make a similar implementation of > Streaming K-medoids? > > K-medoids has even bigger problems than K-means because it's not scalable, > but can be useful in some cases (e.g. It allows more sophisticated distance > measures). > > What is your opinion about implementation of this? > > Best regards, > Marko >
