I noticed that there are two different functions for spectral clustering in sklearn.cluster library: SpectralClustering and spectral_clustering. Although they differ in some details, both do spectral clustering and most of their parameters overlap. I am confused about why there are two methods so similar in sklearn?
Some differences I noticed:
In SpectralClustering, parameter affinity takes both string and array; its default value is 'rbf'; in spectral_clustering it can only be a matrix
SpectralClustering() works like a constructor. It doesn't return anything but has two attributes affinity_matrix_(which you can access after calling .fit()) and labels_. spectral_clustering is a method that only returns the labels.
Using SpectralClustering:
cluster=SpectralClustering().fit(X)
cluster.labels_
Using spectral_clustering:
labels=spectral_clustering(affinity_matrix)
Despite these apparent differences, I'm wondering whether these two methods differ in fundamental aspects. Otherwise why are there two methods that accomplish basically the same thing?
Did you check the source code?
I'd expect that SpectralClustering is an object oriented wrapper for the imperative method spectral_clustering.