gpuHclust              package:gputools              R Documentation

_P_e_r_f_o_r_m _H_i_e_r_a_r_c_h_i_c_a_l _C_l_u_s_t_e_r_i_n_g _f_o_r _V_e_c_t_o_r_s _w_i_t_h _a _G_P_U

_D_e_s_c_r_i_p_t_i_o_n:

     This function performs clustering on a set of points.  The
     distance between each pair of points should be calculated first
     using a function like  'gpuDist' or 'dist'.

_U_s_a_g_e:

             gpuHclust(distances, method = "single")

_A_r_g_u_m_e_n_t_s:

distances: a matrix of floating point numbers whose i, j entry
          represents the distance between point i and point j.  To use
          this with the dist function, be sure to generate the whole
          matrix of distances,  and wrap the output with 'as.matrix'.

  method: 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".

_V_a_l_u_e:

     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.

_S_e_e _A_l_s_o:

     hclust, 'gpuDistClust'

_E_x_a_m_p_l_e_s:

     numVectors <- 5
     dimension <- 10
     Vectors <- matrix(runif(numVectors*dimension), numVectors, dimension)
     distMat <- gpuDist(Vectors, "euclidean")
     gpuHclust(distMat, "single")
     gpuHclust(as.matrix(dist(Vectors)), "single")

