Hi Phillippe,
I used the KMeans on TFIDF 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 mapreduce stages. This requires a bit of tinkering with
the KMeans 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 KMeans 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 <philippe.lamarche@gmail.com>
> Hi,
>
> I have a questions concerning text clustering and the current
> KMeans/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 TFIDF
> vector space, and then I used a little modified version of the
> syntheticcontrol KMeans example. So far, all is fine.
>
> However, the output of the kmean 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 kmeans 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.
>
