pam {cluster}  R Documentation 
Partitioning (clustering) of the data into k
clusters ``around
medoids'', a more robust version of Kmeans.
pam(x, k, diss = inherits(x, "dist"), metric = "euclidean", medoids = NULL, stand = FALSE, cluster.only = FALSE, keep.diss = !diss && !cluster.only && n < 100, keep.data = !diss && !cluster.only, trace.lev = 0)
x 
data matrix or data frame, or dissimilarity matrix or object,
depending on the value of the diss argument.
In case of a matrix or data frame, each row corresponds to an observation, and each column corresponds to a variable. All variables must be numeric. Missing values ( NA s)
are allowed—as long as every pair of observations has at
least one case not missing.
In case of a dissimilarity matrix, x is typically the output
of daisy or dist . Also a vector of
length n*(n1)/2 is allowed (where n is the number of observations),
and will be interpreted in the same way as the output of the
abovementioned functions. Missing values (NAs) are not
allowed.

k 
positive integer specifying the number of clusters, less than the number of observations. 
diss 
logical flag: if TRUE (default for dist or
dissimilarity objects), then x will be considered as a
dissimilarity matrix. If FALSE, then x will be considered as
a matrix of observations by variables.

metric 
character string specifying the metric to be used for calculating
dissimilarities between observations. The currently available options are "euclidean" and "manhattan". Euclidean distances are root sumofsquares of differences, and manhattan distances are the sum of absolute differences. If x is already a dissimilarity matrix, then
this argument will be ignored.

medoids 
NULL (default) or lengthk vector of integer
indices (in 1:n ) specifying initial medoids instead of using
the ‘build’ algorithm. 
stand 
logical; if true, the measurements in x are
standardized before calculating the dissimilarities. Measurements
are standardized for each variable (column), by subtracting the
variable's mean value and dividing by the variable's mean absolute
deviation. If x is already a dissimilarity matrix, then this
argument will be ignored. 
cluster.only 
logical; if true, only the clustering will be computed and returned, see details. 
keep.diss, keep.data 
logicals indicating if the dissimilarities
and/or input data x should be kept in the result. Setting
these to FALSE can give much smaller results and hence even save
memory allocation time. 
trace.lev 
integer specifying a trace level for printing
diagnostics during the build and swap phase of the algorithm.
Default 0 does not print anything; higher values print
increasingly more. 
pam
is fully described in chapter 2 of Kaufman and Rousseeuw
(1990). Compared to the kmeans approach in kmeans
, the
function pam
has the following features: (a) it also accepts a
dissimilarity matrix; (b) it is more robust because it minimizes a sum
of dissimilarities instead of a sum of squared euclidean distances;
(c) it provides a novel graphical display, the silhouette plot (see
plot.partition
) (d) it allows to select the number of clusters
using mean(silhouette(pr))
on the result
pr < pam(..)
, or directly its component
pr$silinfo$avg.width
, see also pam.object
.
When cluster.only
is true, the result is simply a (possibly
named) integer vector specifying the clustering, i.e.,
pam(x,k, cluster.only=TRUE)
is the same as
pam(x,k)$clustering
but computed more efficiently.
The pam
algorithm is based on the search for k
representative objects or medoids among the observations of the
dataset. These observations should represent the structure of the
data. After finding a set of k
medoids, k
clusters are
constructed by assigning each observation to the nearest medoid. The
goal is to find k
representative objects which minimize the sum
of the dissimilarities of the observations to their closest
representative object.
By default, when medoids
are not specified, the algorithm first
looks for a good initial set of medoids (this is called the
build phase). Then it finds a local minimum for the
objective function, that is, a solution such that there is no single
switch of an observation with a medoid that will decrease the
objective (this is called the swap phase).
When the medoids
are specified, their order does not
matter; in general, the algorithms have been designed to not depend on
the order of the observations.
an object of class "pam"
representing the clustering. See
?pam.object
for details.
For datasets larger than (say) 200 observations, pam
will take a lot of
computation time. Then the function clara
is preferable.
agnes
for background and references;
pam.object
, clara
, daisy
,
partition.object
, plot.partition
,
dist
.
## generate 25 objects, divided into 2 clusters. x < rbind(cbind(rnorm(10,0,0.5), rnorm(10,0,0.5)), cbind(rnorm(15,5,0.5), rnorm(15,5,0.5))) pamx < pam(x, 2) pamx summary(pamx) plot(pamx) ## use obs. 1 & 16 as starting medoids  same result (typically) (p2m < pam(x, 2, medoids = c(1,16))) p3m < pam(x, 3, trace = 2) ## rather stupid initial medoids: (p3m. < pam(x, 3, medoids = 3:1, trace = 1)) pam(daisy(x, metric = "manhattan"), 2, diss = TRUE) data(ruspini) ## Plot similar to Figure 4 in Stryuf et al (1996) ## Not run: plot(pam(ruspini, 4), ask = TRUE)