MDS: Multidimensional Scaling (MDS)

In ProjectionBasedClustering: Projection Based Clustering

Multidimensional Scaling (MDS)

Description

Classical multidimensional scaling of a data matrix. Also known as principal coordinates analysis

Usage

MDS(DataOrDistances,method='euclidean',OutputDimension=2,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(dist(Data,method))
method	method specified by distance string: 'euclidean','cityblock=manhatten','cosine','chebychev','jaccard','minkowski','manhattan','binary'
OutputDimension	Number of dimensions in the Outputspace, default=2
PlotIt	Default: FALSE, If TRUE: Plots the projection as a 2d visualization.
Cls	[1:n,1] 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

An short overview of different types of projection methods can be found in [Thrun, 2018, p.42, Fig. 4.1] (\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-3-658-20540-9")}).

Value

ProjectedPoints	[1:n,OutputDimension], n by OutputDimension matrix containing coordinates of the Projection
Eigenvalues	the eigenvalues of MDSvalues*MDSvalues'
Stress	Shephard-Kruskal Stress

Note

A wrapper for cmdscale

You can use the standard ShepardScatterPlot or the better approach through the ShepardDensityPlot of the CRAN package DataVisualizations.

Author(s)

Michael Thrun

Examples

data('Hepta')
Data=Hepta$Data

Proj=MDS(Data)

## Not run: 
PlotProjectedPoints(Proj$ProjectedPoints,Hepta$Cls)

## End(Not run)

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