PolarSwarm: Polar Swarm (Pswarm)

In ProjectionBasedClustering: Projection Based Clustering

Polar Swarm (Pswarm)

Description

Swarm-based Projection method using game theory published in [Thrun/Ultsch, 2020].

Usage

PolarSwarm(DataOrDistances, method = "euclidean", PlotIt = FALSE, Cls)

Arguments

DataOrDistances	Numerical matrix defined as either Data, i.e., [1:n,1:d], nonsymmetric, and consists of n cases of d-dimensional data points with every case having d attributes, variables or features, or Distances, i.e.,[1:n,1:n], symmetric and consists of n cases, e.g., as.matrix(parallelDist::parallelDist(Data,method))
method	If Data is given the method to computing the distances can be specified here. Please see the documentation of package parallelDist for the types that are possible.
PlotIt	Default: FALSE, If TRUE: Plots the projection as a 2d visualization. OutputDimension>2: only the first two dimensions will be shown
Cls	Optional,: only relevant if PlotIt=TRUE. Numeric vector, given Classification in numbers: every element is the cluster number of a certain corresponding element of data.

Details

By exploting swarm intelligence and game theory no parameter have to be set.

Value

List of

ProjectedPoints	[1:n,2], n by 2 matrix containing coordinates of the Projection
ModelObject	output of Pswarm

Author(s)

Michael Thrun

References

[Thrun/Ultsch, 2020] Thrun, M. C., & Ultsch, A.: Swarm Intelligence for Self-Organized Clustering, Artificial intelligence, Vol. 290, pp. 103237, doi 10.1016/j.artint.2020.103237, 2020.

See Also

Pswarm

Examples

data('Hepta')
Data=Hepta$Data

Distances=as.matrix(dist(Data))
Proj=PolarSwarm(Data)
## Not run: 
PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls)
## End(Not run)

ProjectionBasedClustering documentation built on Oct. 12, 2023, 1:07 a.m.