sklearn.cluster.affinity_propagation

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

Perform Affinity Propagation Clustering of data

Parameters:

S : array-like, shape (n_samples, n_samples)

Matrix of similarities between points

preference : array-like, 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

Returns:

cluster_centers_indices : array, shape (n_clusters,)

index of clusters centers

labels : array, shape (n_samples,)

cluster labels for each point

Notes

See examples/cluster/plot_affinity_propagation.py for an example.

References

Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points”, Science Feb. 2007