# Spectral clustering result interpretation

I cluster a toy data set into three groups with spectral clustering. Please see the code below. affinity='rbf' by default.

from sklearn.cluster import SpectralClustering
import numpy as np

points = np.array([[5,6], [3,3], [2,2], [7,3], [8,3], [5,4], [5,5], [9,3], [7,1]])

spectral = SpectralClustering(n_clusters=3)
labels = spectral.fit(points).labels_


The output is three clusters with labels [1 2 2 0 0 1 1 0 2] shown on the image. Why point 8 happens to be in the same cluster with 1 and 2, not with points 3,4, and 7?

UPDATE:

Affinity matrix:

Spectral embedding result:

• How to get Affinity matrix from spectral clustering? Oct 5 '19 at 10:04