Hi Phillippe, Even I had to use an Id for a vector. So, What I did was, I used KeyValueTextInputFormat as the input format (Default is textinputformat) and gave the input as ID \t Vector (ID and vector are tab separated) and made changes at the final display part(runClustering) to consider id too along with the vector.
Thanks Pallavi -----Original Message----- From: Richard Tomsett [mailto:[email protected]] Sent: Thursday, December 04, 2008 5:16 AM To: [email protected] Subject: Re: Text clustering 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 <[email protected]> > 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 TF-IDF > 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. >
