Document Clustering in python using SciKit
For the large matrix after TF/IDF transformation, consider using sparse matrix.
You could try different k values. I am not an expert in unsupervised clustering algorithms, but I bet with such algorithms and different parameters, you could also end up with a varied number of clusters.
I recently started working on Document clustering using SciKit module in python. However I am having a hard time understanding the basics of document clustering. 
What I know ?

Document clustering is typically done using TF/IDF. Which essentially
converts the words in the documents to vector space model which is
then input to the algorithm.
There are many algorithms like k-means, neural networks, hierarchical
clustering to accomplish this.

My Data :

I am experimenting with linkedin data, each document would be the
linkedin profile summary, I would like to see if similar job
documents get clustered together.

Current Challenges: 

My data has huge summary descriptions, which end up becoming 10000's
of words when I apply TF/IDF. Is there any proper way to handle this
high dimensional data.
K - means and other algorithms requires I specify the no. of clusters
( centroids ), in my case I do not know the number of clusters
upfront. This I believe is a completely unsupervised learning. Are
there algorithms which can determine the no. of clusters themselves?
I've never worked with document clustering before, if you are aware
of tutorials , textbooks or articles which address this issue, please
feel free to suggest.

I went through the code on SciKit webpage, it consists of too many technical words which I donot understand, if you guys have any code with good explanation or comments please share. Thanks in advance.