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

In DatabionicSwarm: Swarm Intelligence for Self-Organized Clustering

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,...)

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

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.

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

Note

LC is now automatically estimated; LC is the size of the grid c(Lines,Columns), number of Lines and Columns, default c(NULL,NULL) and automatic calculation by setGridSize

Author(s)

Michael Thrun

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, \Sexpr[results=rd]{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, \Sexpr[results=rd]{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)

DatabionicSwarm documentation built on Oct. 13, 2023, 5:10 p.m.