clustTool-package         package:clustTool         R Documentation

_C_l_u_s_t_e_r_i_n_g _w_i_t_h _s_p_a_t_i_a_l _i_n_f_o_r_m_a_t_i_o_n.

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

     Cluster results can change dramatically depending on the choice of
     the clustering method, the distance measure, and the number of
     clusters. Moreover, depending on the selected validity measure,
     there may be different results for the optimal number of clusters.
     Despite of the changing cluster results, each partition could
     still be informative and valuable. The results can give an
     interesting insight into the multivariate data structure even if
     the validity measure does not suggest the optimum for the chosen
     cluster number. It is thus desirable to perform cluster analysis
     in an exploratory context, by changing the cluster parameters and
     inspecting the results visually.

     For this purpose, this statistical tool has been developed.  Data,
     subsets of the data, coordinates and maps can be selected.
     Furhtermore, different parameters like the distance measure, the
     clustering method, the number of clusters and the validity measure
     can be selected. Depending on the selection the clusters can be
     presented on maps. Additionally, plots of the cluster centres are
     provided.

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


       Package:  clustTool
       Type:     Package
       Version:  1.0
       Date:     2006-08-09
       License:  GPL 2.0 or newer

     Try 

     GUIspatStat

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

     Matthias Templ

     Maintainer: Matthias Templ <templ@statistik.tuwien.ac.at>

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

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

     ## destroy the Rcmdr Commander window
     ## tkdestroy(commanderWindow)
     library(mvoutlier)
     data(kola.background)
     data(humus)
     x <- prepare(humus[,c("As", "Ca", "Co", "Mo", "Ni")])
     cl1 <- clust(x, k=9, method="clara", distMethod="manhattan")
     names(cl1)
     clustPlot(coord=humus[,2:3], clust=cl1, k=cl1$k, val="median.distance", Map="kola.background")
     GUIspatClust()

