clust               package:clustTool               R Documentation

_W_r_a_p_p_e_r _f_u_n_c_t_i_o_n _f_o_r _a _v_a_r_i_e_t_y _o_f _c_l_u_s_t_e_r_i_n_g _a_l_g_o_r_i_t_h_m_s

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

     Performs cluster analysis on data.

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

     clust(x = Cassini$x, k = 3, method = "kmeansHartigan", seed = set.seed(123), distMethod = "euclidean", qtclustsize = 0.7, iter.max = 100, eps = 0.1, vals = TRUE, alt = NULL, coord = NULL, bic = NULL)

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

       x: data frame or matrix 

       k: Number of clusters 

  method: Cluster algorithm 

    seed: Seed (can be useful if results from clustering should be
          reproduced exactly) 

distMethod: Distance Measure 

qtclustsize: Only important if method qtclust is chosen (see
          sQuote{qtclust} in package 'flexclust') 

iter.max: Only important if method kmeans is chosen (see 'kmeans' in
          package 'stats'  

     eps: Only important if method 'dbscan' is chosen 

    vals: Validity measures for the resulting clusters would be
          calculated if this parameter is set to TRUE 

     alt: an integer vector for each observation indicating the cluster
          number for an alternative clustering. If provided, the
          corrected rand index for 'clustering' vs. 'alt.clustering'
          will be computed (see also in package 'fpc'). 

   coord: Cluster validity measures will be calculated based on
          coordinates. 

     bic: Alternative way to specify bic values for each cluster. 

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

     This function acts like a wrapper function for applying a variety
     of clustering algorithms. The function would be carried out from
     the 'clustTool'-GUI. To specify additional parameters for special
     algorithms one should use the algorithm itself and structure the
     output as the output from this function (as class 'clust'
     suggests). 

     Number of Clusters: Since there will be no necessarity for a large
     number of clusters, the maximum number of clusters should not
     exeed 12.

     Cluster algorithms: Possible values are: "kmeansHartigan",
     "kmeansLloyd", "kmeansForgy", "kmeansMacQueen", "cmeans", 
     "cmeansUfcl", "pam", "clara", "fanny", "bclust", "cshell",
     "Mclust", "kccaKmeans", "kccaKmedians", "kccaAngle",
     "kccaJaccard", "kccaEjaccard", "cclustKmeans", "cclustHardcl",
     "cclustNeuralgas", "qtclustKmeans", "qtclustKmedian",
     "qtclustAngle", "qtclustJaccard", "qtclustEjaccard", "dbscan",
     "speccPolydot", "fixmahal", "hclustSingle", "hclustComplete",
     "hclustAverage", "hclustWard", "hclustMcquitty", "hclustMedian",
     "hclustcentroid".

     Cluster algorithms which are supported by 'clustTool'-GUI:
     "kmeansHartigan", "clara", "bclust", "Mclust", "kccaKmeans",
     "speccPolydot", "cclustNeuralgas", "cmeans", "kccaKmedians". 

     For details see the help files listed below.

     distMethod: Possible values are: "euclidean", "manhattan",
     "maximum", "canberra", "cosa", "rf" (dissimilarity measure based
     on random Forest proximity measure), "gower", "bray",
     "kulczynski", "chord", "morisita", "horn", "mountford",
     "correlation" (dissimilarity measure based on correlations).

     Distance measures which are supported by 'spatClust'-GUI:
     "euclidean",
     "manhattan","rf","bray","gower","kulczynski","morisita","correlation".

     For details see the help files listed below.

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

cluster : A vector of integers indicating the cluster to which each
          point is allocated.

 centers: A matrix of cluster centres.

    size: The number of points in each cluster.

   xdata: The input data.

  method: Clustering method

distMethod: Distance measure

       k: Number of clusters

   valTF: logical, if global validity measures provided

valMeasures: global validity measures

silwidths: local validity measure

separation: local validity measure

diameter: local validity measure

average.distance: local validity measure

median.distance: local validity measure

average.toother: local validity measure

      vp: logical, if colnames provided

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

     Matthias Templ

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

     Clustering methods:

     'kmeans', 'cmeans', 'pam', 'clara', 'fanny', 'bclust', 'Mclust',
     'kcca', 'cclust', 'specc', 'hclust'

     Distance measures:

     'dist', 'vegdist',  'g.dist', 'randomForest', "cosa", 'cor'

     Cluster validity measures:

     'cluster.stats'

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

      require(mvoutlier)
      data(humus)
      x <- prepare(humus[,c("As", "Ca", "Co", "Mo", "Ni")])
      cl1 <- clust(x, k=9, method="clara", distMethod="manhattan")
      cl1
      names(cl1)

