I have a 3000x50 feature vector matrix. I obtained a similarity matrix for this using sklearn.metrics.pairwise_distances
as 'Similarity_Matrix'. Now I used networkx
to create a graph using the similarity matrix generated in the previous step as G=nx.from_numpy_matrix(Similarity_Matrix)
. I want to perform spectral clustering on this graph G
now but several google searches have failed to provide a decent example of scikit learn spectral clustering on this graph :( The official documentation shows how spectral clustering can be done on some image data which is highly unclear at least to a newbie like myself.
Can anyone give me a code sample for this or for graph cuts or graph partitioning using networkx, scikit learn etc.
Thanks a million!
pairwise_distances
produces a distance matrix, but you need a similarity matrix (a kernel's Gram matrix). I don't see why you put NetworkX in the loop as well. – Holocaust