affin.prop: Affinity Propagation

In swfscMisc: Miscellaneous Functions for Southwest Fisheries Science Center

Affinity Propagation

Description

Runs the Affinity Propagation clustering algorithm of Frey and Dueck, 2007.

Usage

affin.prop(
  sim.mat,
  num.iter = 100,
  stable.iter = 10,
  shared.pref = "min",
  lambda = 0.5
)

Arguments

sim.mat	a similarity matrix between individuals to be clustered.
num.iter	maximum number of iterations to attempt.
stable.iter	number of sequential iterations for which consistent clustering is considered acceptable.
shared.pref	type of shared preference to use. Can be one of "min", "median", or a numeric value.
lambda	damping factor.

Value

A matrix with one row per sample in 'sim.mat' and one column for each iteration. Values in columns indicate cluster assignment (arbitrary numbers) for each sample.

Author(s)

Eric Archer [e-mail]

References

Frey, B.J., and D. Dueck. 2007. Clustering by passing messages between data points. Science 315:972-976

Examples

data(iris)

# Take 75 random iris rows for example
iris <- iris[sample(1:nrow(iris), 75), ]
iris <- droplevels(iris)

iris.sim <- -dist(iris[, -5])

iris.affin <- affin.prop(iris.sim, stable.iter = 5)
table(iris$Species, iris.affin[, ncol(iris.affin)])

swfscMisc documentation built on Sept. 8, 2023, 5:55 p.m.