APclustering: Affinity Propagation Clustering

In Mthrun/FCPS: Fundamental Clustering Problems Suite

Affinity Propagation Clustering

Description

Affinity propagation clustering published by [Frey/Dueck, 2007] and implemented by [Bodenhofer et al., 2011].

Usage

APclustering(DataOrDistances,

InputPreference=NA,ExemplarPreferences=NA,

DistanceMethod="euclidean",

Seed=7568,PlotIt=FALSE,Data,...)

Arguments

DataOrDistances	[1:n,1:d] with: if d=n and symmetric then distance matrix assumed, otherwise: [1:n,1:d] matrix of dataset to be clustered. It consists of n cases or d-dimensional data points. Every case has d attributes, variables or features. In the latter case the Euclidean distances will be calculated.
InputPreference	Default parameter set, see apcluster
ExemplarPreferences	Default parameter set, see apcluster
DistanceMethod	DistanceMethod as in dist for similarities.
Seed	Set as integervalue to have reproducible results, see apcluster
PlotIt	Default: FALSE, If TRUE and dataset of [1:n,1:d] dimensions then a plot of the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls will be generated.
Data	[1:n,1:d] data matrix in the case that DataOrDistances is missing and partial matching does not work.
...	Further arguments to be set for the clustering algorithm, if not set, default arguments are used.

Details

Distancematrix D is converted to similarity matrix S with S=-(D^2).

If data matrix is used, then euclidean similarities are calculated by similarities and a specifed distance method.

The AP algorithm decides the k number of clusters.

Value

List of

Cls	[1:n] numerical vector with n numbers defining the classification as the main output of the clustering algorithm. It has k unique numbers representing the arbitrary labels of the clustering.
Object	Object defined by clustering algorithm as the other output of this algorithm

Author(s)

Michael Thrun

References

[Frey/Dueck, 2007] Frey, B. J., & Dueck, D.: Clustering by passing messages between data points, Science, Vol. 315(5814), pp. 972-976, <doi:10.1126/science.1136800>, 2007.

[Bodenhofer et al., 2011] Bodenhofer, U., Kothmeier, A., & Hochreiter, S.: APCluster: an R package for affinity propagation clustering, Bioinformatics, Vol. 27(17), pp, 2463-2464, 2011.

Further details in http://www.bioinf.jku.at/software/apcluster/

See Also

apcluster

Examples

data('Hepta')
res=APclustering(Hepta$Data, PlotIt = FALSE)

Mthrun/FCPS documentation built on June 28, 2023, 9:29 a.m.