pam                 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

_D_e_s_c_r_i_p_t_i_o_n:

     Partitioning (clustering) of the data into 'k' clusters ``around
     medoids'', a more robust version of K-means.

_U_s_a_g_e:

     pam(x, k, diss = inherits(x, "dist"),
         metric = "euclidean", stand = FALSE, cluster.only = FALSE,
         keep.diss = !diss && !cluster.only && n < 100,
         keep.data = !diss && !cluster.only)

_A_r_g_u_m_e_n_t_s:

       x: data matrix or data frame, or dissimilarity matrix or object,
          depending on the value of the 'diss' argument.

          In case of a matrix or data frame, each row corresponds to an
          observation, and each column corresponds to a variable.  All
          variables must be numeric.  Missing values ('NA's) _are_
          allowed-as long as every pair of observations has at least
          one case not missing.

          In case of a dissimilarity matrix, 'x' is typically the
          output of 'daisy' or 'dist'.  Also a vector of length
          n*(n-1)/2 is allowed (where n is the number of observations),
          and will be interpreted in the same way as the output of the
          above-mentioned functions. Missing values (NAs) are _not_
          allowed. 

       k: positive integer specifying the number of clusters, less than
          the number of observations.

    diss: logical flag: if TRUE (default for 'dist' or 'dissimilarity'
          objects), then 'x' will be considered as a dissimilarity
          matrix.  If FALSE, then 'x' will be considered as a matrix of
          observations by variables. 

  metric: character string specifying the metric to be used for
          calculating dissimilarities between observations.
           The currently available options are "euclidean" and
          "manhattan".  Euclidean distances are root sum-of-squares of
          differences, and manhattan distances are the sum of absolute
          differences.  If 'x' is already a dissimilarity matrix, then
          this argument will be ignored. 

   stand: logical; if true, the measurements in 'x' are standardized
          before calculating the dissimilarities.  Measurements are
          standardized for each variable (column), by subtracting the
          variable's mean value and dividing by the variable's mean
          absolute deviation.  If 'x' is already a dissimilarity
          matrix, then this argument will be ignored.

cluster.only: logical; if true, only the clustering will be computed
          and returned, see details.

keep.diss, keep.data: logicals indicating if the dissimilarities and/or
          input data 'x' should be kept in the result.  Setting these
          to 'FALSE' can give much smaller results and hence even save
          memory allocation _time_.

_D_e_t_a_i_l_s:

     'pam' is fully described in chapter 2 of Kaufman and Rousseeuw
     (1990).  Compared to the k-means approach in 'kmeans', the
     function 'pam' has the following features: (a) it also accepts a
     dissimilarity matrix; (b) it is more robust because it minimizes a
     sum of dissimilarities instead of a sum of squared euclidean
     distances; (c) it provides a novel graphical display, the
     silhouette plot (see 'plot.partition') (d) it allows to select the
     number of clusters using 'mean(silhouette(pr))' on the result 'pr
     <- pam(..)', or directly its component 'pr$silinfo$avg.width', see
     also 'pam.object'.

     When 'cluster.only' is true, the result is simply a (possibly
     named) integer vector specifying the clustering, i.e.,
      'pam(x,k, cluster.only=TRUE)' is the same as 
      'pam(x,k)$clustering' but computed more efficiently.

     The 'pam'-algorithm is based on the search for 'k' representative
     objects or medoids among the observations of the dataset.  These
     observations should represent the structure of the data.  After
     finding a set of 'k' medoids, 'k' clusters are constructed by
     assigning each observation to the nearest medoid.  The goal is to
     find 'k' representative objects which minimize the sum of the
     dissimilarities of the observations to their closest
     representative object. 
      The algorithm first looks for a good initial set of medoids (this
     is called the *build* phase).  Then it finds a local minimum for
     the objective function, that is, a solution such that there is no
     single switch of an observation with a medoid that will decrease
     the objective (this is called the *swap* phase).

_V_a_l_u_e:

     an object of class '"pam"' representing the clustering.  See
     '?pam.object' for details.

_N_o_t_e:

     For datasets larger than (say) 200 observations, 'pam' will take a
     lot of computation time.  Then the function 'clara' is preferable.

_S_e_e _A_l_s_o:

     'agnes' for background and references; 'pam.object', 'clara',
     'daisy', 'partition.object', 'plot.partition', 'dist'.

_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)))
     pamx <- pam(x, 2)
     pamx
     summary(pamx)
     plot(pamx)

     pam(daisy(x, metric = "manhattan"), 2, diss = TRUE)

     data(ruspini)
     ## Plot similar to Figure 4 in Stryuf et al (1996)
     ## Not run: plot(pam(ruspini, 4), ask = TRUE)

