Affinity Propagation

Affinity propagation is a clustering algorithm based on message passing between data points. Similar to K-medoids, it finds a subset of points as exemplars based on (dis)similarities, and assigns each point in the given data set to the closest exemplar.

This package implements the affinity propagation algorithm 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.

The implementation is optimized by reducing unnecessary array allocation and fusing loops. Specifically, the algorithm is implemented by the affinityprop function:

affinityprop(S; ...)

Performs affinity propagation based on a similarity matrix S.

Parameters:S – The similarity matrix. Here, S[i,j] is the similarity (or negated distance) between samples i and j when i != j; while S[i,i] reflects the availability of the i-th sample as an exemplar.

This function returns an instance of AffinityPropResult, defined as below:

type AffinityPropResult <: ClusteringResult
    exemplars::Vector{Int}      # indexes of exemplars (centers)
    assignments::Vector{Int}    # assignments for each point
    iterations::Int             # number of iterations executed
    converged::Bool             # converged or not
end

One may optionally specify the following keyword arguments:

name	description	default
maxiter	Maximum number of iterations.	100
tol	Tolerable change of objective at convergence.	1.0e-6
damp	Dampening coefficient. The value should be in [0.0, 1.0). Larger value of damp indicates slower (and probably more stable) update. When damp = 0, it means no dampening is performed.	0.5
display	The level of information to be displayed (see Common Options)	:none