Is there an efficient python implementation of spectral clustering for large, dense matrices?
I'd recommend performing PCA to project the data to a lower dimensionality , and then utilize mini batch k-means
Currently I'm using the spectral clustering method from sklearn for my dense 7000x7000 matrix which performs very slowly and exceeds an execution time of 6 hours. Is there a faster implementation of spectral clustering in python?