python : facing memory issue in document clustering using sklearn
start small.
First cluster only 100.00 documents. Only once it works (because it probably won't), then think about scaling up.
If you don't succeed clustering the subset (and text clusters are usually pretty bad), then you won't fare well on the large set.
I am using TfIdfVectorizer of sklearn for document clustering. I have 20 million texts, for which i want to compute clusters. But calculating TfIdf matrix is taking too much time and system is getting stuck. 
Is there any technique to deal with this problem ? is there any alternative method for this in any python module ?