sklearn.cluster.affinity_propagation

sklearn.cluster.affinity_propagation(S, preference=None, convergence_iter=15, max_iter=200, damping=0.5, copy=True, verbose=False, return_n_iter=False)
[source]

Perform Affinity Propagation Clustering of data
Read more in the User Guide.
Parameters: 

S : arraylike, shape (n_samples, n_samples) 
Matrix of similarities between points 
preference : arraylike, shape (n_samples,) or float, optional 
Preferences for each point  points with larger values of preferences are more likely to be chosen as exemplars. The number of exemplars, i.e. of clusters, is influenced by the input preferences value. If the preferences are not passed as arguments, they will be set to the median of the input similarities (resulting in a moderate number of clusters). For a smaller amount of clusters, this can be set to the minimum value of the similarities. 
convergence_iter : int, optional, default: 15 
Number of iterations with no change in the number of estimated clusters that stops the convergence. 
max_iter : int, optional, default: 200 
Maximum number of iterations 
damping : float, optional, default: 0.5 
Damping factor between 0.5 and 1. 
copy : boolean, optional, default: True 
If copy is False, the affinity matrix is modified inplace by the algorithm, for memory efficiency 
verbose : boolean, optional, default: False 
The verbosity level 
return_n_iter : bool, default False 
Whether or not to return the number of iterations. 
Returns: 

cluster_centers_indices : array, shape (n_clusters,) 
index of clusters centers 
labels : array, shape (n_samples,) 
cluster labels for each point 
n_iter : int 
number of iterations run. Returned only if return_n_iter is set to True. 
Notes
For an example, see examples/cluster/plot_affinity_propagation.py.
When the algorithm does not converge, it returns an empty array as cluster_center_indices
and 1
as label for each training sample.
When all training samples have equal similarities and equal preferences, the assignment of cluster centers and labels depends on the preference. If the preference is smaller than the similarities, a single cluster center and label 0
for every sample will be returned. Otherwise, every training sample becomes its own cluster center and is assigned a unique label.
References
Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points”, Science Feb. 2007
Examples using sklearn.cluster.affinity_propagation