And, of course, due credit should be given here. The advanced clustering algorithms in Crunch were lifted from the new stuff in Mahout pretty much step for step.
The Mahout group would have loved to have contributions from the Cloudera guys instead of re-implementation, but you can't legislate taste. On Wed, Mar 27, 2013 at 1:46 PM, Harsh J <ha...@cloudera.com> wrote: > 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 <yaron.go...@gmail.com> > 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 >
