gpuDistClust {gputools} | R Documentation |
Compute Distances and Hierarchical Clustering for Vectors on a GPU
Description
This function takes a set of vectors and performs clustering
on them. The function will first calculate the distance between all of the
pairs of vectors and then use the distances to cluster the vectors. Both of
these steps are done on the GPU.
Usage
gpuDistClust(points, distmethod = "euclidean", clustmethod = "single")
Arguments
points |
a matrix of floating point numbers in which each row is a
vector in $R^n$ space where $n$ is ncol(points). |
distmethod |
a string representing the name of the metric to use to
calculate the distance between the vectors of 'points'. Currently
supported values are: "binary", "canberra", "euclidean", "manhattan",
"maximum". |
clustmethod |
a string representing the name of the clustering method
to be applied to distances. Currently supported method names include
"average", "centroid", "complete", "flexible", "flexible group",
"mcquitty", "median", "single", "ward", and "wpgma". |
Value
Copied from the native R function 'hclust' documentation. A list with
the following 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. Copied from the native R function 'hclust'
documentation. |
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. |
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. |
See Also
gpuDist
, gpuHclust
.
Examples
numVectors <- 5
dimension <- 10
Vectors <- matrix(runif(numVectors*dimension), numVectors, dimension)
gpuDistClust(Vectors, "euclidean", "complete")
gpuDistClust(Vectors, "maximum", "mcquitty")
[Package
gputools version 0.1-0
Index]