6.8.4. scikits.learn.cluster.AffinityPropagation¶
- class scikits.learn.cluster.AffinityPropagation(damping=0.5, max_iter=200, convit=30, copy=True)¶
Perform Affinity Propagation Clustering of data
Parameters : damping : float, optional
Damping factor
max_iter : int, optional
Maximum number of iterations
convit : int, optional
Number of iterations with no change in the number of estimated clusters that stops the convergence.
copy: boolean, optional :
Make a copy of input data. True by default.
Notes
See examples/plot_affinity_propagation.py for an example.
Reference:
Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points”, Science Feb. 2007
The algorithmic complexity of affinity propagation is quadratic in the number of points.
Attributes
cluster_centers_indices_ array, [n_clusters] Indices of cluster centers labels_ array, [n_samples] Labels of each point Methods
fit: Compute the clustering - __init__(damping=0.5, max_iter=200, convit=30, copy=True)¶
- fit(S, p=None, **params)¶
compute MeanShift
Parameters : S: array [n_points, n_points] :
Matrix of similarities between points
p: array [n_points,] or float, optional :
Preferences for each point
damping : float, optional
Damping factor
copy: boolean, optional :
If copy is False, the affinity matrix is modified inplace by the algorithm, for memory efficiency