Spectral clustering

Spectral clustering algorithm uses the graph distance in the nearest neighbor graph. It does a low-dimension embedding of the affinity matrix between samples, followed by a k-means in the low dimensional space.

Affinity measure

Try with nearest neighbors Try with RBF kernel Try with polynomial kernel

Gamma

Kernel coefficient for RBF and polynomial kernels.
Gamma defines the 'influence' of each training example in the features space.
A low value of gamma means that each example has 'far-reaching influence', while a high value means that each example only has close-range influence.
If no value is specified (or 0.0), then 1/nb_features is used.

Coef0

Independent term for 'polynomial' or 'sigmoid' kernel function.

Seed

Used to generate reproducible results. 0 or no value means that no known seed is used (results will not be fully reproducible)