clusplot.partition          package:cluster          R Documentation

_B_i_v_a_r_i_a_t_e _C_l_u_s_p_l_o_t _o_f _a _P_a_r_t_i_t_i_o_n_i_n_g _O_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     Clusplot (Clustering Plot) method for an object of class
     'partition'.

_U_s_a_g_e:

     ## S3 method for class 'partition':
     clusplot(x, main = NULL, dist = NULL, ...)

_A_r_g_u_m_e_n_t_s:

       x: an object of class '"partition"', e.g. created by the
          functions 'pam', 'clara', or 'fanny'.

    main: title for the plot; when 'NULL' (by default), a title is
          constructed, using 'x$call'.

    dist: when 'x' does not have a 'diss' nor a 'data' component, e.g.,
          for 'pam(dist(*), keep.diss=FALSE)', 'dist' must specify the
          dissimilarity for the clusplot.

     ...: all optional arguments available for the 'clusplot.default'
          function (except for the 'diss' one) may also be supplied to
          this function.  Graphical parameters (see 'par') may also be
          supplied as arguments to this function.

_D_e_t_a_i_l_s:

     This 'clusplot.partition()' method relies on 'clusplot.default'.

     If the clustering algorithms 'pam', 'fanny' and 'clara' are
     applied to a data matrix of observations-by-variables then a
     clusplot of the resulting clustering can always be drawn.  When
     the data matrix contains missing values and the clustering is
     performed with 'pam' or 'fanny', the dissimilarity matrix will be
     given as input to 'clusplot'.  When the clustering algorithm
     'clara' was applied to a data matrix with NAs then clusplot will
     replace the missing values as described in 'clusplot.default',
     because a dissimilarity matrix is not available.

_V_a_l_u_e:

     An invisible list with components 

Distances: When option lines is 1 or 2 we optain a k by k matrix (k is
          the number of clusters). The element at row j and column s is
          the distance between ellipse j and ellipse s.  If lines=0,
          then the value of this component is NA. 

 Shading: A vector of length k (where k is the number of clusters),
          containing the amount of shading per cluster.  Let y be a
          vector where element i is the ratio between the number of
          objects in cluster i and the area of ellipse i.  When the
          cluster i is a line segment, y[i] and the density of the
          cluster are set to NA.  Let z be the sum of all the elements
          of y without the NAs.  Then we put shading = y/z *37 + 3. 

_S_e_e _A_l_s_o:

     'clusplot.default' for references; 'partition.object', 'pam',
     'pam.object', 'clara', 'clara.object', 'fanny', 'fanny.object',
     'par'.

_E_x_a_m_p_l_e_s:

     ## generate 25 objects, divided into 2 clusters.
     x <- rbind(cbind(rnorm(10,0,0.5), rnorm(10,0,0.5)),
                cbind(rnorm(15,5,0.5), rnorm(15,5,0.5)))
     clusplot(pam(x, 2))
     ## add noise, and try again :
     x4 <- cbind(x, rnorm(25), rnorm(25))
     clusplot(pam(x4, 2))