Available distance measures are (written for two vectors $x$ and
$y$):
- Euclidean: Usual square distance between the two vectors (2 norm).
- Pearson:$1- \mbox{cor}(x,y)$
- Pearson not centered:$1 - \frac{\sum_i x_i y_i}{\left(\sum_i x_i^2
\sum_i y_i^2 \right) ^{1/2}}$
Xcluster does not use usual agglomerative methods (single, average, complete),
but compute the distance between each
groups' barycenter for the distance between two groups.This have a problem for this kind of data:
rll{
A 0 0
B 0 1
C 0.9 0.5
}
Ie: a triangular in {R$^2$, the distance between A and B is larger
than the distance between the group A,B and C (with euclidean distance).
For that case it can be useful to use clean=TRUE and that mean
that you must not consider A and B as a group without C.
}
An object of class hclust which describes the
tree produced by the clustering process.
The object is a list with components:
- merge
{an $n-1$ by 2 matrix.
Row $i$ of merge describes the merging of clusters
at step $i$ of the clustering.
If an element $j$ in the row is negative,
then observation $-j$ was merged at this stage.
If $j$ is positive then the merge
was with the cluster formed at the (earlier) stage $j$
of the algorithm.
Thus negative entries in merge indicate agglomerations
of singletons, and positive entries indicate agglomerations
of non-singletons.}
- height
{a set of $n-1$ non-decreasing real values.
The clustering height: that is, the value of
the criterion associated with the clustering
method for the particular agglomeration.}
- order
{a vector giving the permutation of the original
observations suitable for plotting, in the sense that a cluster
plot using this ordering and matrix merge will not have
crossings of the branches.}
- labels
{labels for each of the objects being clustered.}
- call
{the call which produced the result.}
- method
{the cluster method that has been used.}
- dist.method
{the distance that has been used to create d
(only returned if the distance object has a "method"
attribute).}
Xcluster is a C program made by Gavin Sherlock that performs
hierarchical clustering, K-means and SOM.
Xcluster is copyrighted.
To get or have information
about
Xcluster: http://genome-www.stanford.edu/~sherlock/cluster.html
# Create data
.Random.seed <- c(1, 416884367 ,1051235439)
m <- matrix(rep(1,3*24),ncol=3)
m[9:16,3] <- 3 ; m[17:24,] <- 3 #create 3 groups
m <- m+rnorm(24*3,0,0.5) #add noise
m <- floor(10*m)/10 #just one digits
# And once you have Xcluster program:
#
#h <- xcluster(m)
#
#plot(h)
cluster
[object Object],[object Object]
r2xcluster, xcluster2r,hclust, hcluster