gpuDistClust: Compute Distances and Hierarchical Clustering for Vectors on...
In gputools: A Few GPU Enabled Functions

Description Usage Arguments Value See Also Examples

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.

1	gpuDistClust(points, distmethod = "euclidean", clustmethod = "complete")

`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".

Copied from the native R function 'hclust' documentation. A class of type "hclust" with the following attributes.

`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.

gpuDist, gpuHclust.

numVectors <- 5
dimension <- 10
Vectors <- matrix(runif(numVectors*dimension), numVectors, dimension)
myClust <- gpuDistClust(Vectors, "maximum", "mcquitty")
plot(myClust)