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From Grant Ingersoll <>
Subject Re: Text clustering
Date Fri, 05 Dec 2008 02:27:18 GMT
I seem to recall some discussion a while back about being able to add  
labels to the vectors/matrices, but I don't know the status of the  

At any rate, very cool that you are using it for text clustering.  I  
still have on my list to write up how to do this and to write some  
supporting code as well.  So, if either of you cares to contribute,  
that would be most useful.


On Dec 3, 2008, at 6:46 PM, Richard Tomsett wrote:

> Hi Phillippe,
> I used the K-Means on TF-IDF vectors and wondered the same thing -  
> about
> labelling the documents. I haven't got my code on me at the moment  
> and it
> was a few months ago that I last looked at it (so I was also  
> probably using
> an older version of Mahout)... but I seem to remember that I did  
> just as you
> are suggesting and simply attached a unique ID to each document  
> which got
> passed through the map-reduce stages. This requires a bit of  
> tinkering with
> the K-Means implementation but shouldn't be too much work.
> As for having massive vectors, you could try representing them as  
> sparse
> vectors rather than the dense vectors the standard Mahout K-Means  
> algorithm
> accepts, which gets rid of all the zero values in the document  
> vectors. See
> the Javadoc for details, it'll be more reliable than my memory :-)
> Richard
> 2008/12/3 Philippe Lamarche <>
>> Hi,
>> I have a questions concerning text clustering and the current
>> K-Means/vectors implementation.
>> For a school project, I did some text clustering with a subset of  
>> the Enron
>> corpus. I implemented a small M/R package that transforms text into  
>> vector space, and then I used a little modified version of the
>> syntheticcontrol K-Means example. So far, all is fine.
>> However, the output of the k-mean algorithm is vector, as is the  
>> input. As
>> I
>> understand it, when text is transformed in vector space, the  
>> cardinality of
>> the vector is the number of word in your global dictionary, all  
>> word in all
>> text being clustered. This, can grow up pretty quick. For example,  
>> with
>> only
>> 27000 Enron emails, even when removing word that only appears in 2  
>> emails
>> or
>> less, the dictionary size is about 45000 words.
>> My number one problem is this: how can we find out what document a  
>> vector
>> is
>> representing, when it comes out of the k-means algorithm? My favorite
>> solution would be to have a unique id attached to each vector. Is  
>> there
>> such
>> ID in the vector implementation? Is there a better solution? Is my  
>> approach
>> to text clustering wrong?
>> Thanks for the help,
>> Philippe.

Grant Ingersoll

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