DatabionicSwarm (version 2.0.0)

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, Cls = NULL, QuadOrHexa = "Hexa", NumJumps = 4,
LC = NULL, Parallel = FALSE, NCores = "max", Verbose = 1,
PlotIt = FALSE, Debug = FALSE, DistanceMeasure = "euclidean",
Eps = 0.001)

Value

List with

ProjectedPoints

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

LC

number of Lines and Columns in c(Lines, Columns)

Control

List, only for intern debugging

Arguments

DataOrDistance

Numeric matrix nxd. Two cases here: d=n => assuming distance matrix d!=n => assuming data matrix with n cases and d features implying the need to compute the distance matrix internally.

Cls

Numeric vector [1:n] with class labels for each observation in DataOrDistance.

QuadOrHexa

Optional, Boolean indicating the geometry of tiles the 2D projection plane is built with.

NumJumps

Integer indicating the number of jumps to be considered for each single databot selected for jumping.

LC

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

Parallel

Optional, Boolean: TRUE = parallel execution, FALSE = single thread execution.

NCores

Character or integer: choice of number of cores of CPU (in case). Can be 'max' or a number. The max will always be 'all available cores - 1', to avoid core overload.

PlotIt

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

Debug

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

DistanceMeasure

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

Verbose

optional, integer stating the degree of textual feedback. 0 = no output, 1 = basic notifications, 2 = progress bar, 3 = details.

Eps

optional, double: Stop criterion for convergence of each epoche.

Author

Michael Thrun, Quirin Stier

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.

[Stier/Thrun, 2024] Stier, Q. and Thrun, M. C.: An efficient multicore CPU implementation of the DatabionicSwarm, 18th conference of the International Federation of Classification Societies (IFCS), San José, Costa Rica, July 14-19, 2024.

Examples

# \donttest{
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 = 50),10,5)
distance=as.matrix(dist(data))
res1=Pswarm(DataOrDistance = distance, Parallel = FALSE, LC = c(10,12))
res2=Pswarm(DataOrDistance = distance, Parallel = TRUE,  LC = c(10,12))
# }