xclara {cluster} R Documentation

## Bivariate Data Set with 3 Clusters

### Description

An artificial data set consisting of 3000 points in 3 quite well-separated clusters.

### Usage

data(xclara)

### Format

A data frame with 3000 observations on 2 numeric variables (named V1 and V2) giving the x and y coordinates of the points, respectively.

### Note

Our version of the xclara is slightly more rounded than the one from read.table("xclara.dat") and the relative difference measured by all.equal is 1.15e-7 for V1 and 1.17e-7 for V2 which suggests that our version has been the result of a options(digits = 7) formatting.

Previously (before May 2017), it was claimed the three cluster were each of size 1000, which is clearly wrong. pam(*, 3) gives cluster sizes of 899, 1149, and 952, which apart from seven “outliers” (or “mislabellings”) correspond to observation indices \{1:900\}, \{901:2050\}, and \{2051:3000\}, see the example.

### Source

Sample data set accompanying the reference below (file ‘xclara.dat’ in side ‘clus_examples.tar.gz’).

### References

Anja Struyf, Mia Hubert & Peter J. Rousseeuw (1996) Clustering in an Object-Oriented Environment. Journal of Statistical Software 1. doi:10.18637/jss.v001.i04

### Examples

## Visualization: Assuming groups are defined as {1:1000}, {1001:2000}, {2001:3000}
plot(xclara, cex = 3/4, col = rep(1:3, each=1000))
p.ID <- c(78, 1411, 2535) ## PAM's medoid indices  == pam(xclara, 3)$id.med text(xclara[p.ID,], labels = 1:3, cex=2, col=1:3) px <- pam(xclara, 3) ## takes ~2 seconds cxcl <- px$clustering ; iCl <- split(seq_along(cxcl), cxcl)
boxplot(iCl, range = 0.7, horizontal=TRUE,
main = "Indices of the 3 clusters of  pam(xclara, 3)")

## Look more closely now:
bxCl <- boxplot(iCl, range = 0.7, plot=FALSE)
## We see 3 + 2 + 2 = 7  clear "outlier"s  or "wrong group" observations:
with(bxCl, rbind(out, group))
## out   1038 1451 1610   30  327  562  770
## group    1    1    1    2    2    3    3
## Apart from these, what are the robust ranges of indices? -- Robust range:
t(iR <- bxCl\$stats[c(1,5),])
##    1  900
##  901 2050
## 2051 3000