silhouette: Compute or Extract Silhouette Information from Clustering

Description

Compute silhouette information according to a given clustering in $k$ clusters.

Usage

silhouette(x, ...)
# S3 method for default
silhouette  (x, dist, dmatrix, ...)
# S3 method for partition
silhouette(x, ...)
# S3 method for clara
silhouette(x, full = FALSE, subset = NULL, ...)
sortSilhouette(object, ...)
# S3 method for silhouette
summary(object, FUN = mean, ...)
# S3 method for silhouette
plot(x, nmax.lab = 40, max.strlen = 5,
     main = NULL, sub = NULL, xlab = expression("Silhouette width "* s[i]),
     col = "gray",  do.col.sort = length(col) > 1, border = 0,
     cex.names = par("cex.axis"), do.n.k = TRUE, do.clus.stat = TRUE, ...)

Value

silhouette() returns an object, sil, of class

silhouette which is an $n \times 3$ matrix with attributes. For each observation i, sil[i,] contains the cluster to which i belongs as well as the neighbor cluster of i (the cluster, not containing i, for which the average dissimilarity between its observations and i is minimal), and the silhouette width $s (i)$ of the observation. The colnames correspondingly are

c("cluster", "neighbor", "sil_width").

summary(sil) returns an object of class

summary.silhouette, a list with components

si.summary:: numerical summary of the individual silhouette widths $s (i)$ .
clus.avg.widths:: numeric (rank 1) array of clusterwise means of silhouette widths where mean = FUN is used.
avg.width:: the total mean FUN(s) where s are the individual silhouette widths.
clus.sizes:: table of the $k$ cluster sizes.
call:: if available, the call creating sil.
Ordered:: logical identical to attr(sil, "Ordered"), see below.

sortSilhouette(sil) orders the rows of sil as in the silhouette plot, by cluster (increasingly) and decreasing silhouette width $s (i)$ .

attr(sil, "Ordered") is a logical indicating if sil

ordered as by sortSilhouette(). In that case,

rownames(sil) will contain case labels or numbers, and

attr(sil, "iOrd") the ordering index vector.

Arguments

x: an object of appropriate class; for the default method an integer vector with $k$ different integer cluster codes or a list with such an x$clustering component. Note that silhouette statistics are only defined if $2 \leq k \leq n - 1$ .
dist: a dissimilarity object inheriting from class dist or coercible to one. If not specified, dmatrix must be.
dmatrix: a symmetric dissimilarity matrix ( $n \times n$ ), specified instead of dist, which can be more efficient.
full: logical or number in $[0, 1]$ specifying if a full silhouette should be computed for clara object. When a number, say $f$ , for a random sample.int(n, size = f*n) of the data the silhouette values are computed. This requires $O ((f * n)^{2})$ memory, since the full dissimilarity of the (sub)sample (see daisy) is needed internally.
subset: a subset from 1:n, specified instead of full to specify the indices of the observations to be used for the silhouette computations.
object: an object of class silhouette.
...: further arguments passed to and from methods.
FUN: function used to summarize silhouette widths.
nmax.lab: integer indicating the number of labels which is considered too large for single-name labeling the silhouette plot.
max.strlen: positive integer giving the length to which strings are truncated in silhouette plot labeling.
main, sub, xlab: arguments to title; have a sensible non-NULL default here.
col, border, cex.names: arguments passed barplot(); note that the default used to be col = heat.colors(n), border = par("fg") instead.
col can also be a color vector of length $k$ for clusterwise coloring, see also do.col.sort:
do.col.sort: logical indicating if the colors col should be sorted “along” the silhouette; this is useful for casewise or clusterwise coloring.
do.n.k: logical indicating if $n$ and $k$ “title text” should be written.
do.clus.stat: logical indicating if cluster size and averages should be written right to the silhouettes.

Details

For each observation i, the silhouette width $s (i)$ is defined as follows:
Put a(i) = average dissimilarity between i and all other points of the cluster to which i belongs (if i is the only observation in its cluster, $s (i) := 0$ without further calculations). For all other clusters C, put $d (i, C)$ = average dissimilarity of i to all observations of C. The smallest of these $d (i, C)$ is $b (i) := min_{C} d (i, C)$ , and can be seen as the dissimilarity between i and its “neighbor” cluster, i.e., the nearest one to which it does not belong. Finally, $s (i) := \frac{b (i) - a (i)}{m a x (a (i), b (i))} .$

silhouette.default() is now based on C code donated by Romain Francois (the R version being still available as cluster:::silhouetteR).

Observations with a large $s (i)$ (almost 1) are very well clustered, a small $s (i)$ (around 0) means that the observation lies between two clusters, and observations with a negative $s (i)$ are probably placed in the wrong cluster.

References

Rousseeuw, P.J. (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math., 20, 53--65.

chapter 2 of Kaufman and Rousseeuw (1990), see the references in plot.agnes.

Examples

Run this code

data(ruspini)
pr4 <- pam(ruspini, 4)
str(si <- silhouette(pr4))
(ssi <- summary(si))
plot(si) # silhouette plot
plot(si, col = c("red", "green", "blue", "purple"))# with cluster-wise coloring

si2 <- silhouette(pr4$clustering, dist(ruspini, "canberra"))
summary(si2) # has small values: "canberra"'s fault
plot(si2, nmax= 80, cex.names=0.6)

op <- par(mfrow= c(3,2), oma= c(0,0, 3, 0),
          mgp= c(1.6,.8,0), mar= .1+c(4,2,2,2))
for(k in 2:6)
   plot(silhouette(pam(ruspini, k=k)), main = paste("k = ",k), do.n.k=FALSE)
mtext("PAM(Ruspini) as in Kaufman & Rousseeuw, p.101",
      outer = TRUE, font = par("font.main"), cex = par("cex.main")); frame()

## the same with cluster-wise colours:
c6 <- c("tomato", "forest green", "dark blue", "purple2", "goldenrod4", "gray20")
for(k in 2:6)
   plot(silhouette(pam(ruspini, k=k)), main = paste("k = ",k), do.n.k=FALSE,
        col = c6[1:k])
par(op)

## clara(): standard silhouette is just for the best random subset
data(xclara)
set.seed(7)
str(xc1k <- xclara[ sample(nrow(xclara), size = 1000) ,]) # rownames == indices
cl3 <- clara(xc1k, 3)
plot(silhouette(cl3))# only of the "best" subset of 46
## The full silhouette: internally needs large (36 MB) dist object:
sf <- silhouette(cl3, full = TRUE) ## this is the same as
s.full <- silhouette(cl3$clustering, daisy(xc1k))
stopifnot(all.equal(sf, s.full, check.attributes = FALSE, tolerance = 0))
## color dependent on original "3 groups of each 1000": % __FIXME ??__
plot(sf, col = 2+ as.integer(names(cl3$clustering) ) %/% 1000,
     main ="plot(silhouette(clara(.), full = TRUE))")

## Silhouette for a hierarchical clustering:
ar <- agnes(ruspini)
si3 <- silhouette(cutree(ar, k = 5), # k = 4 gave the same as pam() above
    	           daisy(ruspini))
stopifnot(is.data.frame(di3 <- as.data.frame(si3)))
plot(si3, nmax = 80, cex.names = 0.5)
## 2 groups: Agnes() wasn't too good:
si4 <- silhouette(cutree(ar, k = 2), daisy(ruspini))
plot(si4, nmax = 80, cex.names = 0.5)

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