8.1.1. sklearn.cluster.AffinityPropagation¶
- class sklearn.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.
References:
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(S[, p]) Compute MeanShift clustering. set_params(**params) Set the parameters of the estimator. - __init__(damping=0.5, max_iter=200, convit=30, copy=True)¶
- fit(S, p=None)¶
Compute MeanShift clustering.
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
- set_params(**params)¶
Set the parameters of the estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :