pam.object              package:cluster              R Documentation

_P_a_r_t_i_t_i_o_n_i_n_g _A_r_o_u_n_d _M_e_d_o_i_d_s (_P_A_M) _O_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     The objects of class '"pam"' represent a partitioning of a dataset
     into clusters.

_V_a_l_u_e:

     A legitimate 'pam' object is a list with the following components: 

 medoids: the medoids or representative objects of the clusters. If a
          dissimilarity matrix was given as input to 'pam', then a
          vector of numbers or labels of observations is given, else
          'medoids' is a matrix with in each row the coordinates of one
          medoid.

  id.med: integer vector of _indices_ giving the medoid observation
          numbers.

clustering: the clustering vector, see 'partition.object'.

objective: the objective function after the first and second step of
          the 'pam' algorithm.

isolation: vector with length equal to the number of clusters,
          specifying which clusters are isolated clusters (L- or
          L*-clusters) and which clusters are not isolated.
           A cluster is an L*-cluster iff its diameter is smaller than
          its separation.  A cluster is an L-cluster iff for each
          observation i the maximal dissimilarity between i and any
          other observation of the cluster is smaller than the minimal
          dissimilarity between i and any observation of another
          cluster.  Clearly each L*-cluster is also an L-cluster. 

clusinfo: matrix, each row gives numerical information for one cluster.
          These are the cardinality of the cluster (number of
          observations), the maximal and average dissimilarity between
          the observations in the cluster and the cluster's medoid, 
          the diameter of the cluster (maximal dissimilarity between
          two observations of the cluster), and the separation of the
          cluster (minimal dissimilarity between an observation of the
          cluster and an observation of another cluster). 

 silinfo: list with silhouette width information, see
          'partition.object'.

    diss: dissimilarity (maybe NULL), see 'partition.object'.

    call: generating call, see 'partition.object'.

    data: (possibibly standardized) see 'partition.object'.

_G_E_N_E_R_A_T_I_O_N:

     These objects are returned from 'pam'.

_M_E_T_H_O_D_S:

     The '"pam"' class has methods for the following generic functions:
     'print', 'summary'.

_I_N_H_E_R_I_T_A_N_C_E:

     The class '"pam"' inherits from '"partition"'. Therefore, the
     generic functions 'plot' and 'clusplot' can be used on a 'pam'
     object.

_S_e_e _A_l_s_o:

     'pam', 'dissimilarity.object', 'partition.object',
     'plot.partition'.

_E_x_a_m_p_l_e_s:

     ## Use the silhouette widths for assessing the best number of clusters,
     ## following a one-dimensional example from Christian Hennig :
     ##
     x <- c(rnorm(50), rnorm(50,mean=5), rnorm(30,mean=15))
     asw <- numeric(20)
     ## Note that "k=1" won't work!
     for (k in 2:20)
       asw[k] <- pam(x, k) $ silinfo $ avg.width
     k.best <- which.max(asw)
     cat("silhouette-optimal number of clusters:", k.best, "\n")

     plot(1:20, asw, type= "h", main = "pam() clustering assessment",
          xlab= "k  (# clusters)", ylab = "average silhouette width")
     axis(1, k.best, paste("best",k.best,sep="\n"), col = "red", col.axis = "red")