| Title: | Hierarchical Clustering with Prototypes |
|---|---|
| Description: | Performs minimax linkage hierarchical clustering. Every cluster has an associated prototype element that represents that cluster as described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," The Journal of the American Statistical Association, 106(495), 1075-1084. |
| Authors: | Jacob Bien and Rob Tibshirani |
| Maintainer: | Jacob Bien <[email protected]> |
| License: | GPL-2 |
| Version: | 1.6.4 |
| Built: | 2024-09-01 03:30:42 UTC |
| Source: | https://github.com/jacobbien/protoclust |
Functions to perform minimax linkage hierarchical clustering and to cut such trees to return clusterings with prototypes.
| Package: | protoclust |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2011-06-21 |
| License: | GPL-2 |
| LazyLoad: | yes |
Jacob Bien and Rob Tibshirani
Maintainer: Jacob Bien <[email protected]>
Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," The Journal of the American Statistical Association, 106(495), 1075-1084.
protoclust, protocut,
plotwithprototypes
# generate some data: set.seed(1) n <- 100 p <- 2 x <- matrix(rnorm(n * p), n, p) rownames(x) <- paste("A", 1:n, sep="") d <- dist(x) # perform minimax linkage clustering: hc <- protoclust(d) # cut the tree to yield a 10-cluster clustering: k <- 10 # number of clusters cut <- protocut(hc, k=k) h <- hc$height[n - k] # plot dendrogram (and show cut): plotwithprototypes(hc, imerge=cut$imerge, col=2) abline(h=h, lty=2) # get the prototype assigned to each point: pr <- cut$protos[cut$cl] # find point farthest from its prototype: dmat <- as.matrix(d) ifar <- which.max(dmat[cbind(1:n, pr[1:n])]) # note that this distance is exactly h: stopifnot(dmat[ifar, pr[ifar]] == h) # since this is a 2d example, make 2d display: plot(x, type="n") points(x, pch=20, col="lightblue") lines(rbind(x[ifar, ], x[pr[ifar], ]), col=3) points(x[cut$protos, ], pch=20, col="red") text(x[cut$protos, ], labels=hc$labels[cut$protos], pch=19) tt <- seq(0, 2 * pi, length=100) for (i in cut$protos) { lines(x[i, 1] + h * cos(tt), x[i, 2] + h * sin(tt)) }# generate some data: set.seed(1) n <- 100 p <- 2 x <- matrix(rnorm(n * p), n, p) rownames(x) <- paste("A", 1:n, sep="") d <- dist(x) # perform minimax linkage clustering: hc <- protoclust(d) # cut the tree to yield a 10-cluster clustering: k <- 10 # number of clusters cut <- protocut(hc, k=k) h <- hc$height[n - k] # plot dendrogram (and show cut): plotwithprototypes(hc, imerge=cut$imerge, col=2) abline(h=h, lty=2) # get the prototype assigned to each point: pr <- cut$protos[cut$cl] # find point farthest from its prototype: dmat <- as.matrix(d) ifar <- which.max(dmat[cbind(1:n, pr[1:n])]) # note that this distance is exactly h: stopifnot(dmat[ifar, pr[ifar]] == h) # since this is a 2d example, make 2d display: plot(x, type="n") points(x, pch=20, col="lightblue") lines(rbind(x[ifar, ], x[pr[ifar], ]), col=3) points(x[cut$protos, ], pch=20, col="red") text(x[cut$protos, ], labels=hc$labels[cut$protos], pch=19) tt <- seq(0, 2 * pi, length=100) for (i in cut$protos) { lines(x[i, 1] + h * cos(tt), x[i, 2] + h * sin(tt)) }
A protoclust object has a prototype associated with each interior node. Every element being clustered occurs at least as a leaf but might also appear multiple times as a prototype. This function finds for each element the path from the root to the highest occurrence of that element. The path is specified by a series of 0s and 1s, where 0 means "go left" and 1 means "go right".
find_elements(hc)find_elements(hc)
hc |
a protoclust object |
paths |
a list of length n giving, for each element, the path from root to its highest occurrence. A 0 means go left, a 1 means go right. |
int_paths |
a list of length n - 1 giving, for each interior node, the path from root to it. A 0 means go left, a 1 means go right. |
Calls plotwithprototypes, which allows one to add prototype
labels to the dendrogram.
## S3 method for class 'protoclust' plot(x, ...)## S3 method for class 'protoclust' plot(x, ...)
x |
a protoclust object |
... |
additional arguments to be passed to
|
Makes a plot of the dendrogram (using plot.hclust) and adds labels of
prototypes on the interior nodes of a dendrogram.
plotwithprototypes( hc, imerge = -seq(n), labels = NULL, bgcol = "white", font = 1, col = 1, cex = 1, ... )plotwithprototypes( hc, imerge = -seq(n), labels = NULL, bgcol = "white", font = 1, col = 1, cex = 1, ... )
hc |
an object of class |
imerge |
a vector of the nodes whose prototype labels should be added.
Interior nodes are numbered from 1 (lowest merge) to n - 1 (highest merge,
i.e. the root) and leaf-nodes are negative (so if element i is a prototype
for a singleton cluster, then -i is included in imerge). Example:
|
labels |
an optional character vector of length n giving the labels of
the elements clustered. If not provided, hc$labels is used (if not NULL) or
else labels are taken to be |
bgcol |
background color for prototype labels |
col, font
|
color and font of prototype labels |
cex |
size of prototype label |
... |
additional arguments to be passed to |
This function lets one put prototype labels on a dendrogram. The argument
imerge controls which interior nodes and leaves are labeled. A
convenient choice for the argument imerge is the imerge-output
of protocut. This allows one to label a dendrogram with the
prototypes of a particular cut. See examples below. This function is
called when one writes plot(hc), where hc is an object of
class protoclust.
Jacob Bien and Rob Tibshirani
Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," The Journal of the American Statistical Association, 106(495), 1075-1084.
# generate some data: set.seed(1) n <- 100 p <- 2 x <- matrix(rnorm(n * p), n, p) rownames(x) <- paste("A", 1:n, sep="") d <- dist(x) # perform minimax linkage clustering: hc <- protoclust(d) # cut the tree to yield a 10-cluster clustering: k <- 10 # number of clusters cut <- protocut(hc, k=k) h <- hc$height[n - k] # plot dendrogram (and show cut): plotwithprototypes(hc, imerge=cut$imerge) # or more simply: plot(hc, imerge=cut$imerge) abline(h=h, lty=2) # negative values of imerge specify which leaves to label k2 <- 20 # more clusters... with two singletons cut2 <- protocut(hc, k=k2) h2 <- hc$height[n - k2] plot(hc, hang=-1, imerge=cut2$imerge) abline(h=h2, lty=2)# generate some data: set.seed(1) n <- 100 p <- 2 x <- matrix(rnorm(n * p), n, p) rownames(x) <- paste("A", 1:n, sep="") d <- dist(x) # perform minimax linkage clustering: hc <- protoclust(d) # cut the tree to yield a 10-cluster clustering: k <- 10 # number of clusters cut <- protocut(hc, k=k) h <- hc$height[n - k] # plot dendrogram (and show cut): plotwithprototypes(hc, imerge=cut$imerge) # or more simply: plot(hc, imerge=cut$imerge) abline(h=h, lty=2) # negative values of imerge specify which leaves to label k2 <- 20 # more clusters... with two singletons cut2 <- protocut(hc, k=k2) h2 <- hc$height[n - k2] plot(hc, hang=-1, imerge=cut2$imerge) abline(h=h2, lty=2)
Makes a plot of the dendrogram (using plot.hclust) and adds interior
node text.
plotwithtext( hc, imerge = -seq(n), text = as.character(1:n), bgcol = "white", font = 1, col = 1, cex = 1, ... )plotwithtext( hc, imerge = -seq(n), text = as.character(1:n), bgcol = "white", font = 1, col = 1, cex = 1, ... )
hc |
an object of class |
imerge |
a vector of the nodes where text is desired.
Interior nodes are numbered from 1 (lowest merge) to n - 1 (highest merge,
i.e. the root) and leaf-nodes are negative. Example:
|
text |
a character vector of length matching 'imerge'. The element 'text[i]' will label node 'imerge[i]'. |
bgcol |
background color for labels |
col, font
|
color and font of labels |
cex |
size of label |
... |
additional arguments to be passed to |
This function lets one put text on a dendrogram. The argument
imerge controls which interior nodes and leaves are labeled with text.
Jacob Bien and Rob Tibshirani
Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," The Journal of the American Statistical Association, 106(495), 1075-1084.
# generate some data: set.seed(1) n <- 100 p <- 2 x <- matrix(rnorm(n * p), n, p) rownames(x) <- paste("A", 1:n, sep="") d <- dist(x) # perform minimax linkage clustering: hc <- protoclust(d) # cut the tree to yield a 10-cluster clustering: k <- 10 # number of clusters cut <- protocut(hc, k=k) h <- hc$height[n - k] # plot dendrogram (and show cut): protoclust:::plotwithtext(hc, imerge=cut$imerge, text=paste("Cluster", 1:k)) abline(h=h, lty=2) # negative values of imerge specify which leaves to label protoclust:::plotwithtext(hc, imerge=c(-1, cut$imerge), text=c("1", paste("Cluster", 1:k)))# generate some data: set.seed(1) n <- 100 p <- 2 x <- matrix(rnorm(n * p), n, p) rownames(x) <- paste("A", 1:n, sep="") d <- dist(x) # perform minimax linkage clustering: hc <- protoclust(d) # cut the tree to yield a 10-cluster clustering: k <- 10 # number of clusters cut <- protocut(hc, k=k) h <- hc$height[n - k] # plot dendrogram (and show cut): protoclust:::plotwithtext(hc, imerge=cut$imerge, text=paste("Cluster", 1:k)) abline(h=h, lty=2) # negative values of imerge specify which leaves to label protoclust:::plotwithtext(hc, imerge=c(-1, cut$imerge), text=c("1", paste("Cluster", 1:k)))
Performs minimax linkage hierarchical clustering given a set of
dissimilarities. Returns an object that looks just like the output of
hclust except that it has an additional element containing prototype
indices.
protoclust(d, verb = FALSE)protoclust(d, verb = FALSE)
d |
dissimilarities object. Can be of class |
verb |
see verbose output? |
This function provides an efficient implementation of minimax linkage hierarchical clustering. Consider two clusters G and H and their union U. The minimax linkage between G and H is defined to be the radius of the smallest ball that encloses all of U and that is centered at one of the points in U. If G and H are merged together, the prototype for the newly formed cluster U is that enclosing ball's center. By construction, the prototype for a cluster will always be one of the objects being clustered. For more on minimax linkage and how one can use prototypes to help interpret a dendrogram, see
An object of class protoclust, which is just like
hclust but has an additional element:
merge, height, order
|
identical to the values returned by |
protos |
a vector of length n - 1. The i-th element is the index of the prototype corresponding to the cluster formed on the i-th merge. |
Jacob Bien and Rob Tibshirani
Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," The Journal of the American Statistical Association, 106(495), 1075-1084.
This function has been designed to work like hclust in terms of
inputs and outputs; however, unlike hclust, it outputs an additional
element, namely a vector of length n - 1 containing the indices of
prototypes. It follows hclust's convention for making the arbitrary
choice of whether to put a subtree on the left or right side.
For cutting a minimax linkage hierarchical clustering, use
protocut, which works like cutree except that it
returns the set of prototypes in addition to the cluster assignments.
This function calls a C implementation of the algorithm detailed in Bien and Tibshirani (2011) that is based on an algorithm described in Murtagh (1983).
Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," The Journal of the American Statistical Association, 106(495), 1075-1084.
Murtagh, F. (1983), "A Survey of Recent Advances in Hierarchical Clustering Algorithms," The Computer Journal, 26, 354–359.
protocut, plotwithprototypes,
hclust
# generate some data: set.seed(1) n <- 100 p <- 2 x <- matrix(rnorm(n * p), n, p) rownames(x) <- paste("A", 1:n, sep="") d <- dist(x) # perform minimax linkage clustering: hc <- protoclust(d) # cut the tree to yield a 10-cluster clustering: k <- 10 # number of clusters cut <- protocut(hc, k=k) h <- hc$height[n - k] # plot dendrogram (and show cut): plotwithprototypes(hc, imerge=cut$imerge, col=2) abline(h=h, lty=2) # get the prototype assigned to each point: pr <- cut$protos[cut$cl] # find point farthest from its prototype: dmat <- as.matrix(d) ifar <- which.max(dmat[cbind(1:n, pr[1:n])]) # note that this distance is exactly h: stopifnot(dmat[ifar, pr[ifar]] == h) # since this is a 2d example, make 2d display: plot(x, type="n") points(x, pch=20, col="lightblue") lines(rbind(x[ifar, ], x[pr[ifar], ]), col=3) points(x[cut$protos, ], pch=20, col="red") text(x[cut$protos, ], labels=hc$labels[cut$protos], pch=19) tt <- seq(0, 2 * pi, length=100) for (i in cut$protos) { lines(x[i, 1] + h * cos(tt), x[i, 2] + h * sin(tt)) }# generate some data: set.seed(1) n <- 100 p <- 2 x <- matrix(rnorm(n * p), n, p) rownames(x) <- paste("A", 1:n, sep="") d <- dist(x) # perform minimax linkage clustering: hc <- protoclust(d) # cut the tree to yield a 10-cluster clustering: k <- 10 # number of clusters cut <- protocut(hc, k=k) h <- hc$height[n - k] # plot dendrogram (and show cut): plotwithprototypes(hc, imerge=cut$imerge, col=2) abline(h=h, lty=2) # get the prototype assigned to each point: pr <- cut$protos[cut$cl] # find point farthest from its prototype: dmat <- as.matrix(d) ifar <- which.max(dmat[cbind(1:n, pr[1:n])]) # note that this distance is exactly h: stopifnot(dmat[ifar, pr[ifar]] == h) # since this is a 2d example, make 2d display: plot(x, type="n") points(x, pch=20, col="lightblue") lines(rbind(x[ifar, ], x[pr[ifar], ]), col=3) points(x[cut$protos, ], pch=20, col="red") text(x[cut$protos, ], labels=hc$labels[cut$protos], pch=19) tt <- seq(0, 2 * pi, length=100) for (i in cut$protos) { lines(x[i, 1] + h * cos(tt), x[i, 2] + h * sin(tt)) }
Cuts a minimax linkage tree to get one of n - 1 clusterings. Works like
cutree except also returns the prototypes of the resulting
clustering.
protocut(hc, k = NULL, h = NULL)protocut(hc, k = NULL, h = NULL)
hc |
an object returned by |
k |
the number of clusters desired |
h |
the height at which to cut the tree |
Given a minimax linkage hierarchical clustering, this function cuts the tree
at a given height or so that a specified number of clusters is created. It
returns both the indices of the prototypes and their locations. This latter
information is useful for plotting a dendrogram with prototypes (see
plotwithprototypes). As with cutree, if both k and h
are given, h is ignored. Unlike cutree, in current version k and h
cannot be vectors.
A list corresponding to the clustering from cutting tree:
cl |
vector of cluster memberships |
protos |
vector of prototype
indices corresponding to the k clusters created. |
imerge |
vector describing the nodes where prototypes occur. We use the
naming convention of the |
Jacob Bien and Rob Tibshirani
Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," The Journal of the American Statistical Association, 106(495), 1075-1084.
protoclust, cutree,
plotwithprototypes
# generate some data: set.seed(1) n <- 100 p <- 2 x <- matrix(rnorm(n * p), n, p) rownames(x) <- paste("A", 1:n, sep="") d <- dist(x) # perform minimax linkage clustering: hc <- protoclust(d) # cut the tree to yield a 10-cluster clustering: k <- 10 # number of clusters cut <- protocut(hc, k=k) h <- hc$height[n - k] # plot dendrogram (and show cut): plotwithprototypes(hc, imerge=cut$imerge, col=2) abline(h=h, lty=2) # get the prototype assigned to each point: pr <- cut$protos[cut$cl] # find point farthest from its prototype: dmat <- as.matrix(d) ifar <- which.max(dmat[cbind(1:n, pr[1:n])]) # note that this distance is exactly h: stopifnot(dmat[ifar, pr[ifar]] == h) # since this is a 2d example, make 2d display: plot(x, type="n") points(x, pch=20, col="lightblue") lines(rbind(x[ifar, ], x[pr[ifar], ]), col=3) points(x[cut$protos, ], pch=20, col="red") text(x[cut$protos, ], labels=hc$labels[cut$protos], pch=19) tt <- seq(0, 2 * pi, length=100) for (i in cut$protos) { lines(x[i, 1] + h * cos(tt), x[i, 2] + h * sin(tt)) }# generate some data: set.seed(1) n <- 100 p <- 2 x <- matrix(rnorm(n * p), n, p) rownames(x) <- paste("A", 1:n, sep="") d <- dist(x) # perform minimax linkage clustering: hc <- protoclust(d) # cut the tree to yield a 10-cluster clustering: k <- 10 # number of clusters cut <- protocut(hc, k=k) h <- hc$height[n - k] # plot dendrogram (and show cut): plotwithprototypes(hc, imerge=cut$imerge, col=2) abline(h=h, lty=2) # get the prototype assigned to each point: pr <- cut$protos[cut$cl] # find point farthest from its prototype: dmat <- as.matrix(d) ifar <- which.max(dmat[cbind(1:n, pr[1:n])]) # note that this distance is exactly h: stopifnot(dmat[ifar, pr[ifar]] == h) # since this is a 2d example, make 2d display: plot(x, type="n") points(x, pch=20, col="lightblue") lines(rbind(x[ifar, ], x[pr[ifar], ]), col=3) points(x[cut$protos, ], pch=20, col="red") text(x[cut$protos, ], labels=hc$labels[cut$protos], pch=19) tt <- seq(0, 2 * pi, length=100) for (i in cut$protos) { lines(x[i, 1] + h * cos(tt), x[i, 2] + h * sin(tt)) }