Partitioning (clustering) of the data into k clusters “around
medoids”, a more robust version of K-means.
pam(x, k, diss = inherits(x, "dist"),
metric = c("euclidean", "manhattan"),
medoids = if(is.numeric(nstart)) "random",
nstart = if(variant == "faster") 1 else NA,
stand = FALSE, cluster.only = FALSE,
do.swap = TRUE,
keep.diss = !diss && !cluster.only && n < 100,
keep.data = !diss && !cluster.only,
variant = c("original", "o_1", "o_2", "f_3", "f_4", "f_5", "faster"),
pamonce = FALSE, trace.lev = 0)an object of class "pam" representing the clustering. See
?pam.object for details.
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 (NAs)
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*(n-1)/2 is allowed (where n is the number of observations),
and will be interpreted in the same way as the output of the
above-mentioned functions. Missing values (NAs) are
not allowed.
positive integer specifying the number of clusters, less than the number of observations.
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.
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 sum-of-squares of
differences, and manhattan distances are the sum of absolute
differences. If x is already a dissimilarity matrix, then
this argument will be ignored.
NULL (default) or length-k vector of integer
indices (in 1:n) specifying initial medoids instead of using
the ‘build’ algorithm.
used only when medoids = "random": specifies the
number of random “starts”; this argument corresponds to
the one of kmeans() (from R's package stats).
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.
logical; if true, only the clustering will be computed and returned, see details.
logical indicating if the swap phase should
happen. The default, TRUE, correspond to the
original algorithm. On the other hand, the swap phase is
much more computer intensive than the build one for large
\(n\), so can be skipped by do.swap = FALSE.
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.
logical or integer in 0:6 specifying algorithmic
short cuts as proposed by Reynolds et al. (2006), and
Schubert and Rousseeuw (2019, 2021) see below.
a character string denoting the variant of
PAM algorithm to use; a more self-documenting version of pamonce
which should be used preferably; note that "faster" not only
uses pamonce = 6 but also nstart = 1 and hence
medoids = "random" by default.
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.
Kaufman and Rousseeuw's orginal Fortran code was translated to C
and augmented in several ways, e.g. to allow cluster.only=TRUE
or do.swap=FALSE, by Martin Maechler.
Matthias Studer, Univ.Geneva provided the pamonce (1 and 2)
implementation.
Erich Schubert, TU Dortmund contributed the pamonce (3 to 6)
implementation.
The basic pam algorithm is fully described in chapter 2 of
Kaufman and Rousseeuw(1990). Compared to the k-means 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)[, "sil_width"]) 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 (i.e. a ‘swap’) that will
decrease the objective (this is called the swap phase).
When the medoids are specified (or randomly generated), their order does not
matter; in general, the algorithms have been designed to not depend on
the order of the observations.
The pamonce option, new in cluster 1.14.2 (Jan. 2012), has been
proposed by Matthias Studer, University of Geneva, based on the
findings by Reynolds et al. (2006) and was extended by Erich Schubert,
TU Dortmund, with the FastPAM optimizations.
The default FALSE (or integer 0) corresponds to the
original “swap” algorithm, whereas pamonce = 1 (or
TRUE), corresponds to the first proposal ....
and pamonce = 2 additionally implements the second proposal as
well.
The key ideas of ‘FastPAM’ (Schubert and Rousseeuw, 2019) are implemented except for the linear approximate build as follows:
pamonce = 3:reduces the runtime by a factor of O(k) by exploiting that points cannot be closest to all current medoids at the same time.
pamonce = 4:additionally allows executing multiple swaps per iteration, usually reducing the number of iterations.
pamonce = 5:adds minor optimizations copied from the
pamonce = 2 approach, and is expected to be the fastest of the
‘FastPam’ variants included.
‘FasterPAM’ (Schubert and Rousseeuw, 2021) is implemented via
pamonce = 6:execute each swap which improves results immediately, and hence typically multiple swaps per iteration; this swapping algorithm runs in \(O(n^2)\) rather than \(O(n(n-k)k)\) time which is much faster for all but small \(k\).
In addition, ‘FasterPAM’ uses random initialization of the medoids (instead of the ‘build’ phase) to avoid the \(O(n^2 k)\) initialization cost of the build algorithm. In particular for large k, this yields a much faster algorithm, while preserving a similar result quality.
One may decide to use repeated random initialization by setting
nstart > 1.
Reynolds, A., Richards, G., de la Iglesia, B. and Rayward-Smith, V. (1992) Clustering rules: A comparison of partitioning and hierarchical clustering algorithms; Journal of Mathematical Modelling and Algorithms 5, 475--504. tools:::Rd_expr_doi("10.1007/s10852-005-9022-1").
Erich Schubert and Peter J. Rousseeuw (2019) Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms; SISAP 2020, 171--187. tools:::Rd_expr_doi("10.1007/978-3-030-32047-8_16").
Erich Schubert and Peter J. Rousseeuw (2021) Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms; Preprint, to appear in Information Systems (https://arxiv.org/abs/2008.05171).
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 # Medoids: '7' and '25' ...
summary(pamx)
plot(pamx)
## use obs. 1 & 16 as starting medoids -- same result (typically)
(p2m <- pam(x, 2, medoids = c(1,16)))
## no _build_ *and* no _swap_ phase: just cluster all obs. around (1, 16):
p2.s <- pam(x, 2, medoids = c(1,16), do.swap = FALSE)
p2.s
p3m <- pam(x, 3, trace = 2)
## rather stupid initial medoids:
(p3m. <- pam(x, 3, medoids = 3:1, trace = 1))
# \dontshow{
ii <- pmatch(c("obj","call"), names(pamx))
stopifnot(all.equal(pamx [-ii], p2m [-ii], tolerance=1e-14),
all.equal(pamx$objective[2], p2m$objective[2], tolerance=1e-14))
# }
pam(daisy(x, metric = "manhattan"), 2, diss = TRUE)
data(ruspini)
## Plot similar to Figure 4 in Stryuf et al (1996)
if (FALSE) plot(pam(ruspini, 4), ask = TRUE)
plot(pam(ruspini, 4))
Run the code above in your browser using DataLab