6.8.3. scikits.learn.cluster.SpectralClustering

class scikits.learn.cluster.SpectralClustering(k=8, mode=None)

Spectral clustering: apply k-means to a projection of the graph laplacian, finds normalized graph cuts.

Parameters :

k: integer, optional :

The dimension of the projection subspace.

mode: {None, ‘arpack’ or ‘amg’} :

The eigenvalue decomposition strategy to use. AMG (Algebraic MultiGrid) is much faster, but requires pyamg to be installed.

Attributes

labels_:

Labels of each point

Methods

fit(X):

Compute spectral clustering

__init__(k=8, mode=None)

fit(X, **params)

Compute the spectral clustering from the adjacency matrix of the graph.

Parameters :

X: array-like or sparse matrix, shape: (p, p) :

The adjacency matrix of the graph to embed. X is an adjacency matrix of a similarity graph: its entries must be positive or zero. Zero means that elements have nothing in common, whereas high values mean that elements are strongly similar.

Notes

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 (heat) kernel:

np.exp(- X**2/2. * delta**2)

If the pyamg package is installed, it is used. This greatly speeds up computation.