nltk.cluster using a sparse representation
It means that when you pass in the input vector, you can either pass in a numpy.array() or a nltk_contrib.unimelb.tacohn.SparseArrays.
I suggest you look at the package nltk_contrib.unimelb.tacohn to find the SparseArrays class. Then try to create your data with this class before passing it into nltk.cluster
I am quite new in Python.
I am trying to use the nltk.cluster package to apply a simple kMeans to a word-document matrix. While it works when the matrix is a list of numpy array-like objects, I wasn't able to make it work for a sparse matrix representation (such as csc_matrix, csr_matrix or lil_matrix). 
All the information that I found was:

Note that the vectors must use numpy array-like objects. nltk_contrib.unimelb.tacohn.SparseArrays may be used for efficiency when required

I do not understand what this means. Can anyone help me in this matter?
Thanks in advance!