Affinity Propagation¶
Affinity propagation is a clustering algorithm based on message passing between data points. Similar to Kmedoids, 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 972976, 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 samplesi
andj
wheni != j
; whileS[i,i]
reflects the availability of thei
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. 1.0e6
damp
Dampening coefficient.
The value should be in
[0.0, 1.0)
. Larger value ofdamp
indicates slower (and probably more stable) update. Whendamp = 0
, it means no dampening is performed.0.5
display
The level of information to be displayed (see Common Options) :none