A bit more concretely, have a look at this class:
http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html
It is a transformer, so you can apply it to any matrix (that doesn't mean
it makes sense, just that you can):
# Create original matrix
X = create_original_matrix_somehow()
# Setup a TfidfTransformer
tfidf = TfidfTransformer()
X_tfidf = tfidf.fit_transform(X)
X_tfidf will then give you the tfidf values.
On 1 July 2014 08:05, Joel Nothman <joel.noth...@gmail.com> wrote:
> It may be beneficial to use some kind of query expansion or unsupervised
> dimensionality reduction, as the vectors from a bag of words encoding will
> probably be very sparse. Does that help?
>
>
> On 30 June 2014 03:03, Abijith Kp <abijith....@gmail.com> wrote:
>
>> Hi,
>>
>> Is it possible to use TfidfVectorizer to cluster very small sized
>> texts?? By small I mean with words less than 20.
>>
>> Or is there any better way to do it.
>>
>> Regards,
>> Abijith
>>
>> --
>> Abijith KP
>> github.com/abijith-kp
>> kpabijith.wordpress.com
>>
>>
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>
>
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Turn processes into business applications with Bonita BPM Community Edition
Quickly connect people, data, and systems into organized workflows
Winner of BOSSIE, CODIE, OW2 and Gartner awards
http://p.sf.net/sfu/Bonitasoft
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