Affinity Propagation

Affinity Propagation

Affinity propagation is a clustering algorithm based on message passing between data points. Similar to K-medoids, it looks at the (dis)similarities in the data, picks one exemplar data point for each cluster, and assigns every point in the data set to the cluster with the closest exemplar.

Clustering.affinityprop — Function.
affinityprop(S::DenseMatrix; [maxiter=200], [tol=1e-6], [damp=0.5],
             [display=:none])

Perform affinity propagation clustering based on a similarity matrix S.

$S_{ij}$ ($i ≠ j$) is the similarity (or the negated distance) between the $i$-th and $j$-th points, $S_{ii}$ defines the availability of the $i$-th point as an exemplar.

Returns an instance of AffinityPropResult.

Method parameters

  • damp::Real: the dampening coefficient, $0 ≤ \mathrm{damp} < 1$. Larger values indicate slower (and probably more stable) update. $\mathrm{damp} = 0$ disables dampening.
  • maxiter, tol, display: see common options

Notes

The implementations is based on the following paper:

Brendan J. Frey and Delbert Dueck. Clustering by Passing Messages Between Data Points. Science, vol 315, pages 972-976, 2007.

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Clustering.AffinityPropResult — Type.

The result of affinity propagation clustering (affinityprop).

Fields

  • exemplars::Vector{Int}: indices of exemplars (cluster centers)
  • assignments::Vector{Int}: cluster assignments for each data point
  • iterations::Int: number of iterations executed
  • converged::Bool: converged or not
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