bclust                 package:e1071                 R Documentation

_B_a_g_g_e_d _C_l_u_s_t_e_r_i_n_g

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

     Cluster the data in `x' using the bagged clustering algorithm. A
     partitioning cluster algorithm such as `kmeans' is run repeatedly
     on bootstrap samples from the original data. The resulting cluster
     centers are then combined using the hierarchical cluster algorithm
     `hclust'.

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

     bclust(x, centers=2, iter.base=10, minsize=0,
            dist.method="euclidian",
            hclust.method="average", base.method="kmeans",
            base.centers=20, verbose=TRUE,
            final.kmeans=FALSE, docmdscale=FALSE,
            resample=TRUE, weights=NULL, maxcluster=base.centers, ...)
     hclust.bclust(object, x, centers, dist.method=object$dist.method,
                   hclust.method=object$hclust.method, final.kmeans=FALSE,
                   docmdscale = FALSE, maxcluster=object$maxcluster)
     plot(x, maxcluster=object$maxcluster, main, ...)
     centers.bclust(object, k)
     clusters.bclust(object, k, x=NULL)

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

       x: Matrix of inputs (or object of class `"bclust"' for plot).

centers, k: Number of clusters.

iter.base: Number of runs of the base cluster algorithm.

 minsize: Minimum number of points in a base cluster.

dist.method: Distance method used for the hierarchical clustering, see
          `dist' for available distances.

hclust.method: Linkage method used for the hierarchical clustering, see
          `hclust' for available methods.

base.method: Partitioning cluster method used as base algorithm.

base.centers: Number of centers used in each repetition of the base
          method.

 verbose: Output status messages.

final.kmeans: If `TRUE', a final kmeans step is performed using the
          output of the bagged clustering as initialization.

docmdscale: Logical, if `TRUE' a `cmdscale' result is included in the
          return value.

resample: Logical, if `TRUE' the base method is run on bootstrap
          samples of `x', else directly on `x'.

 weights: Vector of length `nrow(x)', weights for the resampling. By
          default all observations have equal weight.

maxcluster: Maximum number of clusters memberships are to be computed
          for.

  object: Object of class `"bclust"'.

    main: Main title of the plot.

     ...: Optional arguments top be passed to the base method in
          `bclust', ignored in `plot'.

_D_e_t_a_i_l_s:

     First, `iter.base' bootstrap samples of the original data in `x'
     are created by drawing with replacement. The base cluster method
     is run on each of these samples with `base.centers' centers. The
     `base.method' must be the name of a partitioning cluster function
     returning a list with the same components as the return value of
     `kmeans'.

     This results in a collection of `iter.base * base.centers'
     centers, which are subsequently clustered using the hierarchical
     method `hclust'. Base centers with less than `minsize' points in
     there respective partitions are removed before the hierarchical
     clustering.

     The resulting dendrogram is then cut to produce `centers'
     clusters. Hence, the name of the argument `centers' is a little
     bit misleading as the resulting clusters need not be convex, e.g.,
     when single linkage is used. The name was chosen for compatibility
      with standard partitioning cluster methods such as `kmeans'.

     A new hierarchical clustering (e.g., using another
     `hclust.method') re-using previous base runs can be performed by
     running `hclust.bclust' on the return value of `bclust'.

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

     `bclust' and `hclust.bclust' return objects of class `"bclust"'
     including the components  

  hclust: Return value of the hierarchical clustering of the collection
          of base centers (Object of class `"hclust"').

 cluster: Vector with indices of the clusters the inputs are assigned
          to.

 centers: Matrix of centers of the final clusters. Only useful, if the
          hierarchical clustering method produces convex clusters.

allcenters: Matrix of all `iter.base * base.centers' centers found in
          the base runs.

_A_u_t_h_o_r(_s):

     Friedrich Leisch

_R_e_f_e_r_e_n_c_e_s:

     Friedrich Leisch. Bagged clustering. Working Paper 51, SFB
     ``Adaptive Information Systems and Modeling in Economics and
     Management Science'', August 1999. <URL:
     http://www.ci.tuwien.ac.at/~leisch>

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

     `hclust', `kmeans', `boxplot.bclust'

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

     data(iris)
     bc1 <- bclust(iris[,1:4], 3, base.centers=5)
     plot(bc1)

     table(clusters.bclust(bc1, 3))
     centers.bclust(bc1, 3)

