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From Charles Earl <charlesce...@me.com>
Subject Re: Naïve k-means using hadoop
Date Wed, 27 Mar 2013 14:39:22 GMT
I would think also that starting with centers in some in-memory Hadoop platform like spark
would also be a valid approach. 
I think the spark demo assumes that the data set is cached vs just centers.
C

On Mar 27, 2013, at 9:24 AM, Bertrand Dechoux <dechouxb@gmail.com> wrote:

> And there is also Cascading ;) : http://www.cascading.org/
> But like Crunch, this is Hadoop. Both are 'only' higher APIs for MapReduce.
> 
> As for the number of reducers, you will have to do the math yourself but I highly doubt
that more than one reducer is needed (imho). But you can indeed distribute the work by the
center identifier.
> 
> Bertrand
> 
> 
> On Wed, Mar 27, 2013 at 2:04 PM, Yaron Gonen <yaron.gonen@gmail.com> wrote:
>> Thanks!
>> Bertrand: I don't like the idea of using a single reducer. A better way for me is
to write all the output of all the reducers to the same directory, and then distribute all
the files.
>> I know about Mahout of course, but I want to implement it myself. I will look at
the documentation though.
>> Harsh: I rather stick to Hadoop as much as I can, but thanks! I'll read the stuff
you linked.
>> 
>> 
>> On Wed, Mar 27, 2013 at 2:46 PM, Harsh J <harsh@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.gonen@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
> 
> 
> 
> -- 
> Bertrand Dechoux

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