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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Helping out with the .7 release
Date Wed, 22 Feb 2012 20:42:54 GMT
I would also like to see if we can put an all reduce implementation into this effort. The idea
is that we can use a map only job that does all iteration internally. I think that this could
result in more than an order of magnitude speed up for our clustering codes.  It could also
provide similar benefits for the nascent parallel classifier training work. 

This seems to be a cleanup of a long standing wart in our code but it is reasonable that others
may feel differently. 

Sent from my iPhone

On Feb 22, 2012, at 10:32 AM, Jeff Eastman <jdog@windwardsolutions.com> wrote:

> This refactoring is focused on some of the iterative clustering algorithms which, in
each iteration, load a prior set of clusters ( e.g. clusters-0) and process each input vector
against them to produce a posterior set of clusters (e.g. clusters-1) for the next iteration.
This will result in k-Means, fuzzyK and Dirichlet being collapsed into a ClusterIterator iterating
over a ClusterClassifier using a ClusteringPolicy. You can see these classes in o.a.m.clustering.
They are a work in progress but in-memory, sequential from sequenceFiles and k-means MR work
in tests and can be demonstrated in the DisplayXX examples which employ them.
> 
> Paritosh has also been building a ClusterClassificationDriver (o.a.m.clustering.classify)
which we want to use to factor all of the redundant cluster-data implementations (-cl option)
out of the respective cluster drivers. This will affect Canopy in addition to the above algorithms.
> 
> An imagined benefit of this refactoring comes from the fact that ClusterClassifier extends
AbstractVectorClassifier and implements OnlineLearner. We think this means that a posterior
set of trained Clusters can be used as a component classifier in a semi-supervised classifier
implementation. I suppose we will need to demonstrate this before we go too much further in
the refactoring but Ted, at least, seems to approve of this integration approach between supervised
classification and clustering (unsupervised classification). I don't think it has had a lot
of other eyeballs on it.
> 
> I don't think LDA fits into this subset of clustering algorithms as also do not Canopy
and MeanShift. As you note, it does not produce Clusters but I'd be interested in your reactions
to the above.
> 
> Jeff
> 
> On 2/22/12 9:55 AM, Jake Mannix wrote:
>> So I haven't looked super-carefully at the clustering refactoring work, can
>> someone give a little overview of what
>> the plan is?
>> 
>> The NewLDA stuff is technically in "clustering" and generally works by
>> taking in SeqFile<IW,VW>  documents as the training corpus, and spits out
>> two things: SeqFile<IW,VW>  of a "model" (keyed on topicId, one vector per
>> topic) and a SeqFile<IW,VW>  of "classifications" (keyed on docId, one
>> vector over the topic space for projection onto each topic dimension).
>> 
>> This is similar to how SVD clustering/decomposition works, but with
>> L1-normed outputs instead of L2.
>> 
>> But this seems very different from all of the structures in the rest of
>> clustering.
>> 
>>   -jake
>> 
>> On Wed, Feb 22, 2012 at 7:56 AM, Jeff Eastman<jdog@windwardsolutions.com>wrote:
>> 
>>> Hi Saikat,
>>> 
>>> I agree with Paritosh, that a great place to begin would be to write some
>>> unit tests. This will familiarize you with the code base and help us a lot
>>> with our 0.7 housekeeping release. The new clustering classification
>>> components are going to unify many - but not all - of the existing
>>> clustering algorithms to reduce their complexity by factoring out
>>> duplication and streamlining their integration into semi-supervised
>>> classification engines.
>>> 
>>> Please feel free to post any questions you may have in reading through
>>> this code. This is a major refactoring effort and we will need all the help
>>> we can get. Thanks for the offer,
>>> 
>>> Jeff
>>> 
>>> 
>>> On 2/21/12 10:46 PM, Saikat Kanjilal wrote:
>>> 
>>>> Hi Paritosh,Yes creating the test case would be a great first start,
>>>> however are there other tasks you guys need help with before I can do
>>>> before the test creation, I will sync trunk and start reading through the
>>>> code in the meantime.Regards
>>>> 
>>>>  Date: Wed, 22 Feb 2012 10:57:51 +0530
>>>>> From: pranjan@xebia.com
>>>>> To: dev@mahout.apache.org
>>>>> Subject: Re: Helping out with the .7 release
>>>>> 
>>>>> We are creating clustering as classification components which will help
>>>>> in moving clustering out. Once the component is ready, then the
>>>>> clustering algorithms would need refactoring.
>>>>> The clustering as classification component and the outlier removal
>>>>> component has been created.
>>>>> 
>>>>> Most of it is committed, and rest is available as a patch. See
>>>>> https://issues.apache.org/**jira/browse/MAHOUT-929<https://issues.apache.org/jira/browse/MAHOUT-929>
>>>>> If you will apply the latest patch available on Mahout-929 you can see
>>>>> all that is available now.
>>>>> 
>>>>> If you want, you can help with the test case of
>>>>> ClusterClassificationMapper available in the patch.
>>>>> 
>>>>> On 22-02-2012 10:27, Saikat Kanjilal wrote:
>>>>> 
>>>>>> Hi Guys,I was interested in helping out with the clustering component
>>>>>> of mahout, I looked through the JIRA items below and was wondering
if there
>>>>>> is a specific one that would be good to start with:
>>>>>> 
>>>>>> https://issues.apache.org/**jira/secure/IssueNavigator.**
>>>>>> jspa?reset=true&jqlQuery=**project+%3D+MAHOUT+AND+**
>>>>>> resolution+%3D+Unresolved+AND+**component+%3D+Clustering+**
>>>>>> ORDER+BY+priority+DESC&mode=**hide<https://issues.apache.org/jira/secure/IssueNavigator.jspa?reset=true&jqlQuery=project+%3D+MAHOUT+AND+resolution+%3D+Unresolved+AND+component+%3D+Clustering+ORDER+BY+priority+DESC&mode=hide>
>>>>>> 
>>>>>> I initially was thinking to work on Mahout-930 or Mahout-931 but
could
>>>>>> work on others if needed.
>>>>>> Best Regards
>>>>>> 
>>> 
> 

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