sklearn.cluster.SpectralClustering for clustering(I used the default set for
SpectralClustering, I paste the log below) the data set $X$. What I want is separate them into 10 groups. But the clustering algorithm, classified almost all of them into one group. Only a few of single points have been separated into other groups. If I perform $Kmeans$ directly on the data $X$(normalized), it well separated into 10 groups. Why? I thought that $spectral clustering$ should perform well too. Because Its obvious that there are 10 groups (I use the $PCA$ to visualize the data).
spectral = SpectralClustering(n_clusters=10)
SpectralClustering(affinity='rbf', assign_labels='kmeans', coef0=1, degree=3,
eigen_solver=None, eigen_tol=0.0, gamma=1.0, kernel_params=None,
n_clusters=10, n_init=10, n_neighbors=10, random_state=None)