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Currently the function calculates the graph laplacian and attempts to find the optimal number of clusters via the "eigengap" method, but:
We should probably be using scipy.sparse.csgraph.laplacian() to generate the graph laplacian rather than rolling our own, and
Normalizing the eigenvalues may not be the right move here; it seems like we should just be able to use the raw eigenvalue spectrum. I've experienced a few cases where the results from the function don't make sense based on the input data and I think it might be coming from this normalization procedure...
Essentially, it would be great to test the get_n_clusters() function a bit more to assess performance!
The text was updated successfully, but these errors were encountered:
Currently the function calculates the graph laplacian and attempts to find the optimal number of clusters via the "eigengap" method, but:
scipy.sparse.csgraph.laplacian()
to generate the graph laplacian rather than rolling our own, andEssentially, it would be great to test the
get_n_clusters()
function a bit more to assess performance!The text was updated successfully, but these errors were encountered: