Clusters are one per data point, while topics are not. So the model is slightly different.
You can get the list of topics for each sample using NMF().fit_transform(X).

On 04/28/2015 01:13 PM, C K Kashyap wrote:
Hi everyone,
I am new to scikit. I only feel sad for not knowing it earlier - it's awesome.

I am trying to do the following. Extract topics from a bunch of tweets. I tried NMF (from the sample here - http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf.html) but I was not able to figure out how to list documents corresponding to the extracted topics. Could someone please point me to an example that lists the documents under each topic?

When I got stuck with NMF, I thought of using kmeans (min batch). I am just wondering though if clustering is a reasonable approach for "topics".

I'd really appreciate any advice here.

Thanks,
Kashyap


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