| justClusters {ClassDiscovery} | R Documentation |
Unsupervised clustering algorithms, such as partitioning around medoids
(pam), K-means (kmeans), or
hierarchical clustering (hclust) after cutting the tree,
produce a list of class assignments along with other structure. To
simplify the interface for the BootstrapClusterTest and
PerturbationClusterTest, we have written these routines
that simply extract these cluster assignments.
cutHclust(data, k, method = "average", metric = "pearson") cutPam(data, k) cutKmeans(data, k) cutRepeatedKmeans(data, k, nTimes) repeatedKmeans(data, k, nTimes)
data |
A numerical data matrix |
k |
The number of classes desired from the algorithm |
method |
Any valid linkage method that can be passed to the
hclust function |
metric |
Any valid distance metric that can be passed to the
distanceMatrix function |
nTimes |
An integer; the number of times to repeat the K-means algorithm with a different random starting point |
Each of the clustering routines used here has a different
structure for storing cluster assignments. The kmeans
function stores the assignments in a ‘cluster’ attribute. The
pam function uses a ‘clustering’ attribute. For
hclust, the assigments are produced by a call to the
cutree function.
It has been observed that the K-means algorithm can converge to
different solutions depending on the starting values of the group
centers. We also include a routine (repeatedKmeans) that runs
the K-means algorithm repeatedly, using different randomly generated
staring pints each time, saving the best results.
Each of the cut... functions returns a vector of integer values
representing the cluster assignments found by teh algorithm.
The repeatedKmeans function returns a list x with three
components. The component x$kmeans is the result of the call
to the kmeans function that produced the best fit to the
data. The component x$centers is a matrix containing the list
of group centers that were used in the best call to kmeans.
The component x$withinss contains the sum of the within-group
sums of squares, which is used as the measure of fitness.
Kevin R. Coombes <kcoombes@mdanderson.org>
# simulate data from three different groups d1 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE) d2 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE) d3 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE) dd <- cbind(d1, d2, d3) cutKmeans(dd, k=3) cutKmeans(dd, k=4) cutHclust(dd, k=3) cutHclust(dd, k=4) cutPam(dd, k=3) cutPam(dd, k=4) cutRepeatedKmeans(dd, k=3, nTimes=10) cutRepeatedKmeans(dd, k=4, nTimes=10) # cleanup rm(d1, d2, d3, dd)