DatabionicSwarm (version 1.2.1)

Pswarm: A Swarm of Databots based on polar coordinates (Polar Swarm).

Description

This projetion method is a part of the databionic swarm which uses the nash equlibrium [Thrun/Ultsch, 2021]. Using polar coordinates for agents (here Databots) in two dimensions has many advantages, for further details see [Thrun, 2018] and [Thrun/Ultsch, 2021].

Usage

Pswarm(DataOrDistance,PlotIt=FALSE,Cls=NULL,Silent=TRUE,
Debug=FALSE,LC=c(NULL,NULL),method= "euclidean",Parallel=FALSE,...)

Value

List with

ProjectedPoints

[1:n,1:2] xy cartesian coordinates of projection

LC

number of Lines and Columns in c(Lines,Columns). Lines is a value slightly above the maximum of the x-coordinates and Columns is a value slightly above the maximum of the y-coordinates of ProjectedPoints

Control

List, only for intern debugging

Arguments

DataOrDistance

Numeric matrix [1:n,1:n]: symmetric matrix of dissimilarities, if variable unsymmetric (Numeric matrix [1:d,1:n]) it is assumed as a dataset and the euclidean distances are calculated of d variables and n cases.

PlotIt

Optional, bool, default=FALSE, If =TRUE, Plots the projection during the computation prozess after every nash equlibirum.

Cls

Optional, numeric vector [1:n], given Classification in numbers, only for plotting if PlotIt=TRUE, irrelevant for computations.

Silent

Optional, bool, default=FALSE, If =TRUE results in various console messages

Debug

Optional, Debug, default=FALSE, =TRUE results in various console messages, depricated for CRAN, because cout is not allowed.

LC

Optional, grid size c(Lines, Columns), sometimes it is better to call setGridSize separately.

method

Optional, one of 39 distance methods of parDist of package parallelDist, if Data matrix is chosen above

Parallel

Optional, =TRUE: Parallel C++ implementation, =FALSE C++ implementation

...

Further arguments passed on to the parDist function, e.g. user-defined distance functions

Author

Michael Thrun

Details

DBS is a flexible and robust clustering framework that consists of three independent modules. The first module is the parameter-free projection method Pswarm Pswarm, which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is a parameter-free high-dimensional data visualization technique, which generates projected points on a topographic map with hypsometric colors GeneratePswarmVisualization, called the generalized U-matrix. The third module is a clustering method with no sensitive parameters DBSclustering. The clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole.

References

[Thrun, 2018] Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, doctoral dissertation 2017, Springer, Heidelberg, ISBN: 978-3-658-20539-3, tools:::Rd_expr_doi("10.1007/978-3-658-20540-9"), 2018.

[Thrun/Ultsch, 2021] Thrun, M. C., and Ultsch, A.: Swarm Intelligence for Self-Organized Clustering, Artificial Intelligence, Vol. 290, pp. 103237, tools:::Rd_expr_doi("10.1016/j.artint.2020.103237"), 2021.

Examples

data("Lsun3D")
Data=Lsun3D$Data
Cls=Lsun3D$Cls
InputDistances=as.matrix(dist(Data))
#If not called separately setGridSize() is called in Pswarm
LC=setGridSize(InputDistances)
res=Pswarm(InputDistances,LC=LC,Cls=Cls,PlotIt=TRUE)

# \dontshow{
data=matrix(runif(n = 100),10,10)
distance=as.matrix(dist(data))
res=Pswarm(distance,LC = c(10,12))
# }