viewClusters: PLOT ALL CLUSTERS IN A 2-D PROJECTION SPACE

Description Usage Arguments Value Author(s) References See Also Examples

Description

Plot all clusters in a 2-D projection space.

Usage

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viewClusters(y, cl, outlierLabel=0,
  projMethod="Eigen", xlim=NULL, ylim=NULL,
  xlab="1st projection direction", 
  ylab="2nd projection direction", 
  title="Scatter plot of 2-D Projected Clusters",
  font=2, font.lab=2, cex=1.2, cex.lab=1.2) 

Arguments

y

Data matrix. Rows correspond to observations. Columns correspond to variables.

cl

Cluster membership vector.

outlierLabel

Label for outliers. Outliers are not involved in calculating the projection directions. Outliers will be represented by red triangles in the plot. By default, outlierLabel=0.

projMethod

Method to construct 2-D projection directions. projMethod="Eigen" indicates that we project data to the 2-dimensional space spanned by the first two eigenvectors of the between cluster distance matrix B={2\over k_0}∑_{i=1}^{k_0}Σ_i+{2\over k_0(k_0-1)}∑_{i<j}(θ_i-θ_j) (θ_i-θ_j)^T. projMethod="DMS" indicates that we project data to the 2-dimensional space spanned by the first two eigenvectors of the between cluster distance matrix B=∑_{i=2}^{k_0}∑_{j=1}^{i-1} n_i n_j(θ_i-θ_j)(θ_i-θ_j)^T. “DMS” method is proposed by Dhillon et al. (2002).

xlim

Range of X axis.

ylim

Range of Y axis.

xlab

X axis label.

ylab

Y axis label.

title

Title of the plot.

font

An integer which specifies which font to use for text (see par).

font.lab

The font to be used for x and y labels (see par).

cex

A numerical value giving the amount by which plotting text and symbols should be scaled relative to the default (see par).

cex.lab

The magnification to be used for x and y labels relative to the current setting of 'cex' (see par).

Value

B

Between cluster distance matrix measuring the between cluster variation.

Q

Columns of Q are eigenvectors of the matrix B.

proj

Projected clusters in the 2-D space spanned by the first 2 columns of the matrix Q.

Author(s)

Weiliang Qiu [email protected]
Harry Joe [email protected]

References

Dhillon I. S., Modha, D. S. and Spangler, W. S. (2002) Class visualization of high-dimensional data with applications. computational Statistics and Data Analysis, 41, 59–90.

Qiu, W.-L. and Joe, H. (2006) Separation Index and Partial Membership for Clustering. Computational Statistics and Data Analysis, 50, 585–603.

See Also

plot1DProjection plot2DProjection

Examples

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n1<-50
mu1<-c(0,0)
Sigma1<-matrix(c(2,1,1,5),2,2)
n2<-100
mu2<-c(10,0)
Sigma2<-matrix(c(5,-1,-1,2),2,2)
n3<-30
mu3<-c(10,10)
Sigma3<-matrix(c(3,1.5,1.5,1),2,2)
n4<-10
mu4<-c(0,0)
Sigma4<-50*diag(2)

library(MASS)
set.seed(1234)
y1<-mvrnorm(n1, mu1, Sigma1)
y2<-mvrnorm(n2, mu2, Sigma2)
y3<-mvrnorm(n3, mu3, Sigma3)
y4<-mvrnorm(n4, mu4, Sigma4)
y<-rbind(y1, y2, y3, y4)
cl<-rep(c(1:3, 0), c(n1, n2, n3, n4))

par(mfrow=c(2,1))
viewClusters(y, cl)
viewClusters(y, cl,projMethod="DMS")

clusterGeneration documentation built on May 2, 2019, 6:53 a.m.