gpuDistClust            package:gputools            R Documentation

_C_o_m_p_u_t_e _D_i_s_t_a_n_c_e_s _a_n_d _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 _o_n _a _G_P_U

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

     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.

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

             gpuDistClust(points, distmethod = "euclidean", clustmethod = "single") 

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

  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".

_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:

     'gpuDist', 'gpuHclust'.

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

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

