Pswarm | R Documentation |
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].
Pswarm(DataOrDistance,PlotIt=FALSE,Cls=NULL,Silent=TRUE,
Debug=FALSE,LC=c(NULL,NULL),method= "euclidean",Parallel=FALSE,...)
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
|
method |
Optional, one of 39 distance methods of |
Parallel |
Optional, =TRUE: Parallel C++ implementation, =FALSE C++ implementation |
... |
Further arguments passed on to the |
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.
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 |
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
Michael Thrun
[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.
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)
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