MarkovClustering {FCPS}

Markov Clustering

Description

Graph clustering algorithm introduced by [van Dongen, 2000].

Usage

MarkovClustering(DataOrDistances=NULL,Adjacency=NULL,

Radius=TRUE,DistanceMethod="euclidean",addLoops = TRUE,PlotIt=FALSE,...)

Arguments

DataOrDistances

NULL or: Either [1:n,1:n] symmetric distance matrix or [1:n,1:d] not symmetric data matrix of n cases and d variables

Adjacency

Used if Data is NULL, matrix [1:n,1:n] defining which points are adjacent to each other by the number 1; not adjacent: 0

Radius

Scalar, Radius for unit disk graph (r-ball graph) if adjacency matrix is missing. Automatic estimation can be done either with =TRUE [Ultsch, 2005] or FALSE [Thrun et al., 2016] if Data instead of Distances are given.

DistanceMethod

Optional distance method of data, default is euclid, see parDist for details

addLoops

Logical; if TRUE, self-loops with weight 1 are added to each vertex of x (see mcl of CRAN package MCL).

PlotIt

Default: FALSE, If TRUE plots the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls

...

Further arguments to be set for the clustering algorithm, if not set, default arguments are used.

Details

DataOrDistances is used to compute the Adjecency matrix if this input is missing. Then a unit-disk (R-ball) graph is calculated.

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. Points which cannot be assigned to a cluster will be reported with 0.

Object

Object defined by clustering algorithm as the other output of this algorithm

Author(s)

Michael Thrun

References

[van Dongen, 2000] van Dongen, S.M. Graph Clustering by Flow Simulation. Ph.D. thesis, Universtiy of Utrecht. Utrecht University Repository: http://dspace.library.uu.nl/handle/1874/848, 2000

[Thrun et al., 2016] Thrun, M. C., Lerch, F., Loetsch, J., & Ultsch, A. : Visualization and 3D Printing of Multivariate Data of Biomarkers, in Skala, V. (Ed.), International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), Vol. 24, Plzen, 2016.

[Ultsch, 2005] Ultsch, A.: Pareto density estimation: A density estimation for knowledge discovery, In Baier, D. & Werrnecke, K. D. (Eds.), Innovations in classification, data science, and information systems, (Vol. 27, pp. 91-100), Berlin, Germany, Springer, 2005.

Examples

data('Hepta')
out=MarkovClustering(Data=Hepta$Data,PlotIt=FALSE)