class sklearn.cluster.SpectralClustering(n_clusters=8, eigen_solver=None, random_state=None, n_init=10, gamma=1.0, affinity=’rbf’, n_neighbors=10, eigen_tol=0.0, assign_labels=’kmeans’, degree=3, coef0=1, kernel_params=None, n_jobs=None)
[source]
Apply clustering to a projection to the normalized laplacian.
In practice Spectral Clustering is very useful when the structure of the individual clusters is highly nonconvex or more generally when a measure of the center and spread of the cluster is not a suitable description of the complete cluster. For instance when clusters are nested circles on the 2D plan.
If affinity is the adjacency matrix of a graph, this method can be used to find normalized graph cuts.
When calling fit
, an affinity matrix is constructed using either kernel function such the Gaussian (aka RBF) kernel of the euclidean distanced d(X, X)
:
np.exp(gamma * d(X,X) ** 2)
or a knearest neighbors connectivity matrix.
Alternatively, using precomputed
, a userprovided affinity matrix can be used.
Read more in the User Guide.
Parameters: 


Attributes: 

If you have an affinity matrix, such as a distance matrix, for which 0 means identical elements, and high values means very dissimilar elements, it can be transformed in a similarity matrix that is well suited for the algorithm by applying the Gaussian (RBF, heat) kernel:
np.exp( dist_matrix ** 2 / (2. * delta ** 2))
Where delta
is a free parameter representing the width of the Gaussian kernel.
Another alternative is to take a symmetric version of the k nearest neighbors connectivity matrix of the points.
If the pyamg package is installed, it is used: this greatly speeds up computation.
>>> from sklearn.cluster import SpectralClustering >>> import numpy as np >>> X = np.array([[1, 1], [2, 1], [1, 0], ... [4, 7], [3, 5], [3, 6]]) >>> clustering = SpectralClustering(n_clusters=2, ... assign_labels="discretize", ... random_state=0).fit(X) >>> clustering.labels_ array([1, 1, 1, 0, 0, 0]) >>> clustering SpectralClustering(affinity='rbf', assign_labels='discretize', coef0=1, degree=3, eigen_solver=None, eigen_tol=0.0, gamma=1.0, kernel_params=None, n_clusters=2, n_init=10, n_jobs=None, n_neighbors=10, random_state=0)
fit (X[, y])  Creates an affinity matrix for X using the selected affinity, then applies spectral clustering to this affinity matrix. 
fit_predict (X[, y])  Performs clustering on X and returns cluster labels. 
get_params ([deep])  Get parameters for this estimator. 
set_params (**params)  Set the parameters of this estimator. 
__init__(n_clusters=8, eigen_solver=None, random_state=None, n_init=10, gamma=1.0, affinity=’rbf’, n_neighbors=10, eigen_tol=0.0, assign_labels=’kmeans’, degree=3, coef0=1, kernel_params=None, n_jobs=None)
[source]
fit(X, y=None)
[source]
Creates an affinity matrix for X using the selected affinity, then applies spectral clustering to this affinity matrix.
Parameters: 


fit_predict(X, y=None)
[source]
Performs clustering on X and returns cluster labels.
Parameters: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns: 


sklearn.cluster.SpectralClustering
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html