If you're also a fan of doing things the better way, you can also checkout some Apache Crunch (http://crunch.apache.org) ways of doing this via https://github.com/cloudera/ml (blog post: http://blog.cloudera.com/blog/2013/03/cloudera_ml_data_science_tools/).
On Wed, Mar 27, 2013 at 3:29 PM, Yaron Gonen <[email protected]> wrote: > Hi, > I'd like to implement k-means by myself, in the following naive way: > Given a large set of vectors: > > Generate k random centers from set. > Mapper reads all center and a split of the vectors set and emits for each > vector the closest center as a key. > Reducer calculated new center and writes it. > Goto step 2 until no change in the centers. > > My question is very basic: how do I distribute all the new centers (produced > by the reducers) to all the mappers? I can't use distributed cache since its > read-only. I can't use the context.write since it will create a file for > each reduce task, and I need a single file. The more general issue here is > how to distribute data produced by reducer to all the mappers? > > Thanks. -- Harsh J
